Black Forest Labs FLUX.2 Visual Intelligence for Enterprise Creative now on Microsoft Foundry
Black Forest Labs’ (BFL) FLUX.2 is now available on Microsoft Foundry. Building on FLUX1.1 [pro] and FLUX.1 Kontext [pro], we’re excited to introduce FLUX.2 [pro] which continues to push the frontier for visual intelligence. FLUX.2 [pro] delivers state-of-the-art quality with pre-optimized settings, matching the best closed models for prompt adherence and visual fidelity while generating faster at lower cost. Prompt: "Cinematic film still of a woman walking alone through a narrow Madrid street at night, warm street lamps, cool blue shadows, light rain reflecting on cobblestones, moody and atmospheric, shallow depth of field, natural skin texture, subtle film grain and introspective mood" This prompt shines because it taps into FLUX.2 [pro]'s cinematic‑lighting engine, letting the model fuse warm street‑lamp glow and cool shadows into a visually striking, film‑grade composition. What’s game-changing about FLUX.2 [pro]? FLUX.2 is designed for real-world creative workflows where consistency, accuracy, and iteration speed determine whether AI generation can replace traditional production pipelines. The model understands lighting, perspective, materials, and spatial relationships. It maintains characters and products consistent across up to 10 reference images simultaneously. It adheres to brand constraints like exact hex colors and legible text. The result: production-ready assets with fewer touchups and stronger brand fidelity. What’s New: Production‑grade quality up to 4MP: High‑fidelity, coherent scenes with realistic lighting, spatial logic, and fine detail suitable for product photography and commercial use cases. Multi‑reference consistency: Reference up to 10 images simultaneously with the best character, product, and style consistency available today. Generate dozens of brand-compliant assets where identity stays perfectly aligned shot to shot. Brand‑accurate results: Exact hex‑color matching, reliable typography, and structured controls (JSON, pose guidance) mean fewer manual fixes and stronger brand compliance. Strong prompt fidelity for complex directions: Improved adherence to complex, structured instructions including multi-part prompts, compositional constraints, and JSON-based controls. 32K token context supports long, detailed workflows with exact positioning specifications, physics-aware lighting, and precise compositional requirements in a single prompt. Optimized inference: FLUX.2 [pro] delivers state-of-the-art quality with pre-optimized inference settings, generating faster at lower cost than competing closed models. FLUX.2 transforms creative production economics by enabling workflows that weren't possible with earlier systems. Teams ship complete campaigns in days instead of weeks, with fewer manual touchups and stronger brand fidelity at scale. This performance stems from FLUX.2's unified architecture, which combines generation and editing in a single latent flow matching model. How it Works FLUX.2 combines image generation and editing in a single latent flow matching architecture, coupling a Mistral‑3 24B vision‑language model (VLM) with a rectified flow transformer. The VLM brings real‑world knowledge and contextual understanding, while the flow transformer models spatial relationships, material properties, and compositional logic that earlier architectures struggled to render. FLUX.2’s architecture unifies visual generation and editing, fuses language‑grounded understanding with flow‑based spatial modeling, and delivers production‑ready, brand‑safe images with predictable control especially when you need consistent identity, exact colors, and legible typography at high resolution. Technical details can be found in the FLUX.2 VAE blog post. Top enterprise scenarios & patterns to try with FLUX.2 [pro] The addition of FLUX.2 [pro] is the next step in the evolution for delivering faster, richer, and more controllable generation unlocking a new wave of creative potential for enterprises. Bring FLUX.2 [pro] into your workflow and transform your creative pipeline from concept to production by trying out these patterns: Enterprise scenarios Patterns to try E‑commerce hero shots Start with a small set of references (product front, material/texture, logo). Prompt for a studio hero shot on a white seamless background, three‑quarter view, softbox key + subtle rim light. Include exact hex for brand accents and specify logo placement. Output at 4MP. Product variants at scale Reuse the hero references; ask for specific colorway, angle, and background variants (e.g., “Create {COLOR} variant, {ANGLE} view, {BG} background”). Keep brand hex and logo position constant across variants. Campaign consistency (character/product identity) Provide 5–10 reference images for the character/product (faces, outfits, mood boards). Request the same identity across scenes with consistent lighting/style (e.g., cinematic warm daylight) and defined environments (e.g., urban rooftop). Marketing templates & localization Define a template (e.g., 3‑column grid: left image, right text). Set headline/body sizes (e.g., 24pt/14pt), contrast ≥ 4.5:1, and brand font. Swap localized copy per locale while keeping layout and spacing consistent. Best practices to get to production readiness with Microsoft Foundry FLUX.2 [pro] brings state-of-the-art image quality to your fingertips. In Microsoft Foundry, you can turn those capabilities into predictable, governed outcomes by standardizing templates, managing references, enforcing brand rules, and controlling spend. These practices below leverage FLUX.2 [pro]’s visual intelligence and turn them into repeatable recipes, auditable artifacts, and cost‑controlled processes within a governed Foundry pipeline. Best Practice What to do Foundry tip Approved templates Create 3–5 templates (e.g., hero shot, variant gallery, packaging, social card) with sections for Composition (camera, lighting, environment), Brand (hex colors, logo placement), Typography (font, sizes, contrast), and Output (resolution, format). Store templates in Foundry as approved artifacts; version them and restrict edits via RBAC. Versioned reference sets Keep 3–10 references per subject (product: front/side/texture; talent: face/outfit/mood) and link them to templates. Save references in governed Foundry storage; reference IDs travel with the job metadata. Resolution staging Use a three‑stage plan: Concept (1–2MP) → Review (2–3MP) → Final (4MP). Leverage FLUX.1 [pro] and FLUX1.1 Kontext [pro] before the Final stage for fast iteration and cost control Enforce stage‑based quotas and cap max resolution per job; require approval to move to 4MP. Automated QA & approvals Run post‑generation checks for color match, text legibility, and safe‑area compliance; gate final renders behind a review step. Use Foundry workflows to require sign‑off at the Review stage before Final stage. Telemetry & feedback Track latency, success rate, usage, and cost per render; collect reviewer notes and refine templates. Dashboards in Foundry: monitor job health, cost, and template performance. Foundry Models continues to grow with cutting-edge additions to meet every enterprise need—including models from Black Forest Labs, OpenAI, and more. From models like GPT‑image‑1, FLUX.2 [pro], and Sora 2, Microsoft Foundry has become the place where creators push the boundaries of what’s possible. Watch how Foundry transforms creative workflows with this demo: Customer Stories As seen at Ignite 2025, real‑world customers like Sinyi Realty have already demonstrated the efficiency of Black Forest Lab’s models on Microsoft Foundry by choosing FLUX.1 Kontext [pro] for its superior performance and selective editing. For their new 'Clear All' feature, they preferred a model that preserves the original room structure and simply removes clutter, rather than generating a new space from scratch, saving time and money. Read the story to learn more. “We wanted to stay in the same workspace rather than having to maintain different platforms,” explains TeWei Hsieh, who works in data engineering and data architecture. “By keeping FLUX Kontext model in Foundry, our data scientists and data engineers can work in the same environment.” As customers like Sinyi Realty have already shown, BFL FLUX models raise the bar for speed, precision, and operational efficiency. With FLUX.2 now on Microsoft Foundry, organizations can bring that same competitive edge directly into their own production pipelines. FLUX.2 [pro] Pricing Foundry Models are fully hosted and managed on Azure. FLUX.2 [pro] is available through pay-as-you-go and on Global Standard deployment type with the following pricing: Generated image: The first generated megapixel (MP) is charged $0.03. Each subsequent megapixel is charged $0.015. Reference image(s): We charge $0.015 for each megapixel. Important Notes: For pricing, resolution is always rounded up to the next megapixel, separately for each reference image and for the generated image. 1 megapixel is counted as 1024x1024 pixels For multiple reference images, each reference image is counted as 1 megapixel Images exceeding 4 megapixels are resized to 4 megapixels Reference the Foundry Models pricing page for pricing. Build Trustworthy AI Solutions Black Forest Labs models in Foundry Models are delivered under the Microsoft Product Terms, giving you enterprise-grade security and compliance out of the box. Each FLUX endpoint offers Content Safety controls and guardrails. Runtime protections include built-in content-safety filters, role-based access control, virtual-network isolation, and automatic Azure Monitor logging. Governance signals stream directly into Azure Policy, Purview, and Microsoft Sentinel, giving security and compliance teams real-time visibility. Together, Microsoft's capabilities let you create with more confidence, knowing that privacy, security, and safety are woven into every Black Forest Labs deployment from day one. Getting Started with FLUX.2 in Microsoft Foundry If you don’t have an Azure subscription, you can sign up for an Azure account here. Search for the model name in the model catalog in Foundry under “Build.” FLUX.2-pro Open the model card in the model catalog. Click on deploy to obtain the inference API and key. View your deployment under Build > Models. You should land on the deployment page that shows you the API and key in less than a minute. You can try out your prompts in the playground. You can use the API and key with various clients. Learn More ▶️ RSVP for the next Model Monday LIVE on YouTube or On-Demand 👩💻 Explore FLUX.2 Documentation on Microsoft Learn 👋 Continue the conversation on DiscordBeyond the Model: Empower your AI with Data Grounding and Model Training
Discover how Microsoft Foundry goes beyond foundational models to deliver enterprise-grade AI solutions. Learn how data grounding, model tuning, and agentic orchestration unlock faster time-to-value, improved accuracy, and scalable workflows across industries.Introducing OpenAI’s GPT-image-1.5 in Microsoft Foundry
Developers building with visual AI can often run into the same frustrations: images that drift from the prompt, inconsistent object placement, text that renders unpredictably, and editing workflows that break when iterating on a single asset. That’s why we are excited to announce OpenAI's GPT Image 1.5 is now generally available in Microsoft Foundry. This model can bring sharper image fidelity, stronger prompt alignment, and faster image generation that supports iterative workflows. Starting today, customers can request access to the model and start building in the Foundry platform. Meet GPT Image 1.5 AI driven image generation began with early models like OpenAI's DALL-E, which introduced the ability to transform text prompts into visuals. Since then, image generation models have been evolving to enhance multimodal AI across industries. GPT Image 1.5 represents continuous improvement in enterprise-grade image generation. Building on the success of GPT Image 1 and GPT Image 1 mini, these enhanced models introduce advanced capabilities that cater to both creative and operational needs. The new image models offer: Text-to-image: Stronger instruction following and highly precise editing. Image-to-image: Transform existing images to iteratively refine specific regions Improved visual fidelity: More detailed scenes and realistic rendering. Accelerated creation times: Up to 4x faster generation speed. Enterprise integration: Deploy and scale securely in Microsoft Foundry. GPT Image 1.5 delivers stronger image preservation and editing capabilities, maintaining critical details like facial likeness, lighting, composition, and color tone across iterative changes. You’ll see more consistent preservation of branded logos and key visuals, making it especially powerful for marketing, brand design, and ecommerce workflows—from graphics and logo creation to generating full product catalogs (variants, environments, and angles) from a single source image. Benchmarks Based on an internal Microsoft dataset, GPT Image 1.5 performs higher than other image generation models in prompt alignment and infographics tasks. It focuses on making clear, strong edits – performing best on single-turn modification, delivering the higher visual quality in both single and multi-turn settings. The following results were found across image generation and editing: Text to image Prompt alignment Diagram / Flowchart GPT Image 1.5 91.2% 96.9% GPT Image 1 87.3% 90.0% Qwen Image 83.9% 33.9% Nano Banana Pro 87.9% 95.3% Image editing Evaluation Aspect Modification Preservation Visual Quality Face Preservation Metrics BinaryEval SC (semantic) DINO (Visual) BinaryEval AuraFace Single-turn GPT image 1 99.2% 51.0% 0.14 79.5% 0.30 Qwen image 81.9% 63.9% 0.44 76.0% 0.85 GPT Image 1.5 100% 56.77% 0.14 89.96% 0.39 Multi-turn GPT Image 1 93.5% 54.7% 0.10 82.8% 0.24 Qwen image 77.3% 68.2% 0.43 77.6% 0.63 GPT image 1.5 92.49% 60.55% 0.15 89.46% 0.28 Using GPT Image 1.5 across industries Whether you’re creating immersive visuals for campaigns, accelerating UI and product design, or producing assets for interactive learning GPT Image 1.5 gives modern enterprises the flexibility and scalability they need. Image models can allow teams to drive deeper engagement through compelling visuals, speed up design cycles for apps, websites, and marketing initiatives, and support inclusivity by generating accessible, high‑quality content for diverse audiences. Watch how Foundry enables developers to iterate with multimodal AI across Black Forest Labs, OpenAI, and more: Microsoft Foundry empowers organizations to deploy these capabilities at scale, integrating image generation seamlessly into enterprise workflows. Explore the use of AI image generation here across industries like: Retail: Generate product imagery for catalogs, e-commerce listings, and personalized shopping experiences. Marketing: Create campaign visuals and social media graphics. Education: Develop interactive learning materials or visual aids. Entertainment: Edit storyboards, character designs, and dynamic scenes for films and games. UI/UX: Accelerate design workflows for apps and websites. Microsoft Foundry provides security and compliance with built-in content safety filters, role-based access, network isolation, and Azure Monitor logging. Integrated governance via Azure Policy, Purview, and Sentinel gives teams real-time visibility and control, so privacy and safety are embedded in every deployment. Learn more about responsible AI at Microsoft. Pricing Model Pricing (per 1M tokens) - Global GPT-image-1.5 Input Tokens: $8 Cached Input Tokens: $2 Output Tokens: $32 Cost efficiency improves as well: image inputs and outputs are now cheaper compared to GPT Image 1, enabling organizations to generate and iterate on more creative assets within the same budget. For detailed pricing, refer here. Getting started Learn more about image generation, explore code samples, and read about responsible AI protections here. Try GPT Image 1.5 in Microsoft Foundry and start building multimodal experiences today. Whether you’re designing educational materials, crafting visual narratives, or accelerating UI workflows, these models deliver the flexibility and performance your organization needs.Integrate Custom Azure AI Agents with Copilot Studio and M365 Copilot
Integrating Custom Agents with Copilot Studio and M365 Copilot In today's fast-paced digital world, integrating custom agents with Copilot Studio and M365 Copilot can significantly enhance your company's digital presence and extend your CoPilot platform to your enterprise applications and data. This blog will guide you through the integration steps of bringing your custom Azure AI Agent Service within an Azure Function App, into a Copilot Studio solution and publishing it to M365 and Teams Applications. When Might This Be Necessary: Integrating custom agents with Copilot Studio and M365 Copilot is necessary when you want to extend customization to automate tasks, streamline processes, and provide better user experience for your end-users. This integration is particularly useful for organizations looking to streamline their AI Platform, extend out-of-the-box functionality, and leverage existing enterprise data and applications to optimize their operations. Custom agents built on Azure allow you to achieve greater customization and flexibility than using Copilot Studio agents alone. What You Will Need: To get started, you will need the following: Azure AI Foundry Azure OpenAI Service Copilot Studio Developer License Microsoft Teams Enterprise License M365 Copilot License Steps to Integrate Custom Agents: Create a Project in Azure AI Foundry: Navigate to Azure AI Foundry and create a project. Select 'Agents' from the 'Build and Customize' menu pane on the left side of the screen and click the blue button to create a new agent. Customize Your Agent: Your agent will automatically be assigned an Agent ID. Give your agent a name and assign the model your agent will use. Customize your agent with instructions: Add your knowledge source: You can connect to Azure AI Search, load files directly to your agent, link to Microsoft Fabric, or connect to third-party sources like Tripadvisor. In our example, we are only testing the CoPilot integration steps of the AI Agent, so we did not build out additional options of providing grounding knowledge or function calling here. Test Your Agent: Once you have created your agent, test it in the playground. If you are happy with it, you are ready to call the agent in an Azure Function. Create and Publish an Azure Function: Use the sample function code from the GitHub repository to call the Azure AI Project and Agent. Publish your Azure Function to make it available for integration. azure-ai-foundry-agent/function_app.py at main · azure-data-ai-hub/azure-ai-foundry-agent Connect your AI Agent to your Function: update the "AIProjectConnString" value to include your Project connection string from the project overview page of in the AI Foundry. Role Based Access Controls: We have to add a role for the function app on OpenAI service. Role-based access control for Azure OpenAI - Azure AI services | Microsoft Learn Enable Managed Identity on the Function App Grant "Cognitive Services OpenAI Contributor" role to the System-assigned managed identity to the Function App in the Azure OpenAI resource Grant "Azure AI Developer" role to the System-assigned managed identity for your Function App in the Azure AI Project resource from the AI Foundry Build a Flow in Power Platform: Before you begin, make sure you are working in the same environment you will use to create your Copilot Studio agent. To get started, navigate to the Power Platform (https://make.powerapps.com) to build out a flow that connects your Copilot Studio solution to your Azure Function App. When creating a new flow, select 'Build an instant cloud flow' and trigger the flow using 'Run a flow from Copilot'. Add an HTTP action to call the Function using the URL and pass the message prompt from the end user with your URL. The output of your function is plain text, so you can pass the response from your Azure AI Agent directly to your Copilot Studio solution. Create Your Copilot Studio Agent: Navigate to Microsoft Copilot Studio and select 'Agents', then 'New Agent'. Make sure you are in the same environment you used to create your cloud flow. Now select ‘Create’ button at the top of the screen From the top menu, navigate to ‘Topics’ and ‘System’. We will open up the ‘Conversation boosting’ topic. When you first open the Conversation boosting topic, you will see a template of connected nodes. Delete all but the initial ‘Trigger’ node. Now we will rebuild the conversation boosting agent to call the Flow you built in the previous step. Select 'Add an Action' and then select the option for existing Power Automate flow. Pass the response from your Custom Agent to the end user and end the current topic. My existing Cloud Flow: Add action to connect to existing Cloud Flow: When this menu pops up, you should see the option to Run the flow you created. Here, mine does not have a very unique name, but you see my flow 'Run a flow from Copilot' as a Basic action menu item. If you do not see your cloud flow here add the flow to the default solution in the environment. Go to Solutions > select the All pill > Default Solution > then add the Cloud Flow you created to the solution. Then go back to Copilot Studio, refresh and the flow will be listed there. Now complete building out the conversation boosting topic: Make Agent Available in M365 Copilot: Navigate to the 'Channels' menu and select 'Teams + Microsoft 365'. Be sure to select the box to 'Make agent available in M365 Copilot'. Save and re-publish your Copilot Agent. It may take up to 24 hours for the Copilot Agent to appear in M365 Teams agents list. Once it has loaded, select the 'Get Agents' option from the side menu of Copilot and pin your Copilot Studio Agent to your featured agent list Now, you can chat with your custom Azure AI Agent, directly from M365 Copilot! Conclusion: By following these steps, you can successfully integrate custom Azure AI Agents with Copilot Studio and M365 Copilot, enhancing you’re the utility of your existing platform and improving operational efficiency. This integration allows you to automate tasks, streamline processes, and provide better user experience for your end-users. Give it a try! Curious of how to bring custom models from your AI Foundry to your Copilot Studio solutions? Check out this blogContext-Aware RAG System with Azure AI Search to Cut Token Costs and Boost Accuracy
🚀 Introduction As AI copilots and assistants become integral to enterprises, one question dominates architecture discussions: “How can we make large language models (LLMs) provide accurate, source-grounded answers — without blowing up token costs?” Retrieval-Augmented Generation (RAG) is the industry’s go-to strategy for this challenge. But traditional RAG pipelines often use static document chunking, which breaks semantic context and drives inefficiencies. To address this, we built a context-aware, cost-optimized RAG pipeline using Azure AI Search and Azure OpenAI, leveraging AI-driven semantic chunking and intelligent retrieval. The result: accurate answers with up to 85% lower token consumption. Majorly in this blog we are considering: Tokenization Chunking The Problem with Naive Chunking Most RAG systems split documents by token or character count (e.g., every 1,000 tokens). This is easy to implement but introduces real-world problems: 🧩 Loss of context — sentences or concepts get split mid-idea. ⚙️ Retrieval noise — irrelevant fragments appear in top results. 💸 Higher cost — you often send 5× more text than necessary. These issues degrade both accuracy and cost efficiency. 🧠 Context-Aware Chunking: Smarter Document Segmentation Instead of breaking text arbitrarily, our system uses an LLM-powered preprocessor to identify semantic boundaries — meaning each chunk represents a complete and coherent concept. Example Naive chunking: “Azure OpenAI Service offers… [cut] …integrates with Azure AI Search for intelligent retrieval.” Context-aware chunking: “Azure OpenAI Service provides access to models like GPT-4o, enabling developers to integrate advanced natural language understanding and generation into their applications. It can be paired with Azure AI Search for efficient, context-aware information retrieval.” ✅ The chunk is self-contained and semantically meaningful. This allows the retriever to match queries with conceptually complete information rather than partial sentences — leading to precision and fewer chunks needed per query. Architecture Diagram Chunking Service: Purpose: Transforms messy enterprise data (wikis, PDFs, transcripts, repos, images) into structured, model-friendly chunks for Retrieval-Augmented Generation (RAG). ChallengeChunking FixLLM context limitsBreaks docs into smaller piecesEmbedding sizeKeeps within token boundsRetrieval accuracyGranular, relevant sections onlyNoiseRemoves irrelevant blocksTraceabilityChunk IDs for auditabilityCost/latencyRe-embed only changed chunks The Chunking Flow (End-to-End) The Chunking Service sits in the ingestion pipeline and follows this sequence: Ingestion: Raw text arrives from sources (wiki, repo, transcript, PDF, image description). Token-aware splitting: Large text is cut into manageable pre-chunks with a 100-token overlap, ensuring no semantic drift across boundaries. Semantic segmentation: Each pre-chunk is passed to an Azure OpenAI Chat model with a structured prompt. Output = JSON array of semantic chunks (sectiontitle, speaker, content). Optional overlap injection: Character-level overlap can be applied across chunks for discourse-heavy text like meeting transcripts. Embedding generation: Each chunk is passed to Azure OpenAI Embeddings API (text-embedding-3-small), producing a 1536-dimension vector. Indexing: Chunks (text + vectors) are uploaded to Azure AI Search. Retrieval: During question answering or document generation, the system pulls top-k chunks, concatenates them, and enriches the prompt for the LLM. Resilience & Traceability The service is built to handle real-world pipeline issues. It retries once on rate limits, validates JSON outputs, and fails fast on malformed data instead of silently dropping chunks. Each chunk is assigned a unique ID (chunk_<sequence>_<sourceTag>), making retrieval auditable and enabling selective re-embedding when only parts of a document change. ☁️ Why Azure AI Search Matters Here Azure AI Search (formerly Cognitive Search) is the heart of the retrieval pipeline. Key Roles: Vector Search Engine: Stores embeddings of chunks and performs semantic similarity search. Hybrid Search (Keyword + Vector): Combines lexical and semantic matching for high precision and recall. Scalability: Supports millions of chunks with blazing-fast search latency. Metadata Filtering: Enables fine-grained retrieval (e.g., by document type, author, section). Native Integration with Azure OpenAI: Allows a seamless, end-to-end RAG pipeline without third-party dependencies. In short, Azure AI Search provides the speed, scalability, and semantic intelligence to make your RAG pipeline enterprise-grade. 💡 Importance of Azure OpenAI Azure OpenAI complements Azure AI Search by providing: High-quality embeddings (text-embedding-3-large) for accurate vector search. Powerful generative reasoning (GPT-4o or GPT-4.1) to craft contextually relevant answers. Security and compliance within your organization’s Azure boundary — critical for regulated environments. Together, these two services form the retrieval (Azure AI Search) and generation (Azure OpenAI) halves of your RAG system. 💰 Token Efficiency By limiting the model’s input to only the most relevant, semantically meaningful chunks, you drastically reduce prompt size and cost. Approach Tokens per Query Typical Cost Accuracy Full-document prompt ~15,000–20,000 Very high Medium Fixed-size RAG chunks ~5,000–8,000 Moderate Medium-high Context-aware RAG (this approach) ~2,000–3,000 Low High 💰 Token Cost Reduction Analysis Let’s quantify it: Step Naive Approach (no RAG) Your Approach (Context-Aware RAG) Prompt context size Entire document (e.g., 15,000 tokens) Top 3 chunks (e.g., 2,000 tokens) Tokens per query ~16,000 (incl. user + system) ~2,500 Cost reduction — ~84% reduction in token usage Accuracy Often low (hallucinations) Higher (targeted retrieval) That’s roughly an 80–85% reduction in token usage while improving both accuracy and response speed. 🧱 Tech Stack Overview Component Service Purpose Chunking Engine Azure OpenAI (GPT models) Generate context-aware chunks Embedding Model Azure OpenAI Embedding API Create high-dimensional vectors Retriever Azure AI Search Perform hybrid and vector search Generator Azure OpenAI GPT-4o Produce final answer Orchestration Layer Python / FastAPI / .NET c# Handle RAG pipeline 🔍 The Bottom Line By adopting context-aware chunking and Azure AI Search-powered RAG, you achieve: ✅ Higher accuracy (contextually complete retrievals) 💸 Lower cost (token-efficient prompts) ⚡ Faster latency (smaller context per call) 🧩 Scalable and secure architecture (fully Azure-native) This is the same design philosophy powering Microsoft Copilot and other enterprise AI assistants today. 🧪 Real-Life Example: Context-Aware RAG in Action To bring this architecture to life, let’s walk through a simple example of how documents can be chunked, embedded, stored in Azure AI Search, and then queried to generate accurate, cost-efficient answers. Imagine you want to build an internal knowledge assistant that answers developer questions from your company’s Azure documentation. ⚙️ Step 1: Intelligent Document Chunking We’ll use a small LLM call to segment text into context-aware chunks — rather than fixed token counts //Context Aware Chunking //text can be your retrieved text from any page/ document private async Task<List<SemanticChunk>> AzureOpenAIChunk(string text) { try { string prompt = $@" Divide the following text into logical, meaningful chunks. Each chunk should represent a coherent section, topic, or idea. Return the result as a JSON array, where each object contains: - sectiontitle - speaker (if applicable, otherwise leave empty) - content Do not add any extra commentary or explanation. Only output the JSON array. Do not give content an array, try to keep all in string. TEXT: {text}" var client = GetAzureOpenAIClient(); var chatCompletionsOptions = new ChatCompletionOptions { Temperature = 0, FrequencyPenalty = 0, PresencePenalty = 0 }; var Messages = new List<OpenAI.Chat.ChatMessage> { new SystemChatMessage("You are a text processing assistant."), new UserChatMessage(prompt) }; var chatClient = client.GetChatClient( deploymentName: _appSettings.Agent.Model); var response = await chatClient.CompleteChatAsync(Messages, chatCompletionsOptions); string responseText = response.Value.Content[0].Text.ToString(); string cleaned = Regex.Replace(responseText, @"```[\s\S]*?```", match => { var match1 = match.Value.Replace("```json", "").Trim(); return match1.Replace("```", "").Trim(); }); // Try to parse the response as JSON array of chunks return CreateChunkArray(cleaned); } catch (JsonException ex) { _logger.LogError("Failed to parse GPT response: " + ex.Message); throw; } catch (Exception ex) { _logger.LogError("Error in AzureOpenAIChunk: " + ex.Message); throw; } } 🧠 Step 2: Adding Overlaps for better result We are adding overlapping between chunks for better and accurate answers. Overlapping window can be modified based on the documents. public List<SemanticChunk> AddOverlap(List<SemanticChunk> chunks, string IDText, int overlapChars = 0) { var overlappedChunks = new List<SemanticChunk>(); for (int i = 0; i < chunks.Count; i++) { var current = chunks[i]; string previousOverlap = i > 0 ? chunks[i - 1].Content[^Math.Min(overlapChars, chunks[i - 1].Content.Length)..] : ""; string combinedText = previousOverlap + "\n" + current.Content; var Id = $"chunk_{i + '_' + IDText}"; overlappedChunks.Add(new SemanticChunk { Id = Regex.Replace(Id, @"[^A-Za-z0-9_\-=]", "_"), Content = combinedText, SectionTitle = current.SectionTitle }); } return overlappedChunks; } 🧠 Step 3: Generate and Store Embeddings in Azure AI Search We convert each chunk into an embedding vector and push it to an Azure AI Search index. public async Task<List<SemanticChunk>> AddEmbeddings(List<SemanticChunk> chunks) { var client = GetAzureOpenAIClient(); var embeddingClient = client.GetEmbeddingClient("text-embedding-3-small"); foreach (var chunk in chunks) { // Generate embedding using the EmbeddingClient var embeddingResult = await embeddingClient.GenerateEmbeddingAsync(chunk.Content).ConfigureAwait(false); chunk.Embedding = embeddingResult.Value.ToFloats(); } return chunks; } public async Task UploadDocsAsync(List<SemanticChunk> chunks) { try { var indexClient = GetSearchindexClient(); var searchClient = indexClient.GetSearchClient(_indexName); var result = await searchClient.UploadDocumentsAsync(chunks); } catch (Exception ex) { _logger.LogError("Failed to upload documents: " + ex); throw; } } 🤖 Step 4: Generate the Final Answer with Azure OpenAI Now we combine the top chunks with the user query to create a cost-efficient, context-rich prompt. P.S. : Here in this example we have used semantic kernel agent , in real time any agent can be used and any prompt can be updated. var context = await _aiSearchService.GetSemanticSearchresultsAsync(UserQuery); // Gets chunks from Azure AI Search //here UserQuery is query asked by user/any question prompt which need to be answered. string questionWithContext = $@"Answer the question briefly in short relevant words based on the context provided. Context : {context}. \n\n Question : {UserQuery}?"; var _agentModel = new AgentModel() { Model = _appSettings.Agent.Model, AgentName = "Answering_Agent", Temperature = _appSettings.Agent.Temperature, TopP = _appSettings.Agent.TopP, AgentInstructions = $@"You are a cloud Migration Architect. " + "Analyze all the details from top to bottom in context based on the details provided for the Migration of APP app using Azure Services. Do not assume anything." + "There can be conflicting details for a question , please verify all details of the context. If there are any conflict please start your answer with word - **Conflict**." + "There might not be answers for all the questions, please verify all details of the context. If there are no answer for question just mention - **No Information**" }; _agentModel = await _agentService.CreateAgentAsync(_agentModel); _agentModel.QuestionWithContext = questionWithContext; var modelWithResponse = await _agentService.GetAnswerAsync(_agentModel); 🧠 Final Thoughts Context-aware RAG isn’t just a performance optimization — it’s an architectural evolution. It shifts the focus from feeding LLMs more data to feeding them the right data. By letting Azure AI Search handle intelligent retrieval and Azure OpenAI handle reasoning, you create an efficient, explainable, and scalable AI assistant. The outcome: Smarter answers, lower costs, and a pipeline that scales with your enterprise. Wiki Link: Tokenization and Chunking IP Link: AI Migration AcceleratorOpen AI’s GPT-5.1-codex-max in Microsoft Foundry: Igniting a New Era for Enterprise Developers
Announcing GPT-5.1-codex-max: The Future of Enterprise Coding Starts Now We’re thrilled to announce the general availability of OpenAI's GPT-5.1-codex-max in Microsoft Foundry Models; a leap forward that redefines what’s possible for enterprise-grade coding agents. This isn’t just another model release; it’s a celebration of innovation, partnership, and the relentless pursuit of developer empowerment. At Microsoft Ignite, we unveiled Microsoft Foundry: a unified platform where businesses can confidently choose the right model for every job, backed by enterprise-grade reliability. Foundry brings together the best from OpenAI, Anthropic, xAI, Black Forest Labs, Cohere, Meta, Mistral, and Microsoft’s own breakthroughs, all under one roof. Our partnership with Anthropic is a testament to our commitment to giving developers access to the most advanced, safe, and high-performing models in the industry. And now, with GPT-5.1-codex-max joining the Foundry family, the possibilities for intelligent applications and agentic workflows have never been greater. GPT 5.1-codex-max is available today in Microsoft Foundry and accessible in Visual Studio Code via the Foundry extension . Meet GPT-5.1-codex-max: Enterprise-Grade Coding Agent for Complex Projects GPT-5.1-codex-max is engineered for those who build the future. Imagine tackling complex, long-running projects without losing context or momentum. GPT-5.1-codex-max delivers efficiency at scale, cross-platform readiness, and proven performance with top scores on SWE-Bench (77.9), the gold standard for AI coding. With GPT-5.1-codex-max, developers can focus on creativity and problem-solving, while the model handles the heavy lifting. GPT-5.1-codex-max isn’t just powerful; it’s practical, designed to solve real challenges for enterprise developers: Multi-Agent Coding Workflows: Automate repetitive tasks across microservices, maintaining shared context for seamless collaboration. Enterprise App Modernization: Effortlessly refactor legacy .NET and Java applications into cloud-native architectures. Secure API Development: Generate and validate secure API endpoints, with `compliance checks built-in for peace of mind. Continuous Integration Support: Integrate GPT-5.1-codex-max into CI/CD pipelines for automated code reviews and test generation, accelerating delivery cycles. These use cases are just the beginning. GPT-5.1-codex-max is your partner in building robust, scalable, and secure solutions. Foundry: Platform Built for Developers Who Build the Future Foundry is more than a model catalog—it’s an enterprise AI platform designed for developers who need choice, reliability, and speed. • Choice Without Compromise: Access the widest range of models, including frontier models from leading model providers. • Enterprise-Grade Infrastructure: Built-in security, observability, and governance for responsible AI at scale. • Integrated Developer Experience: From GitHub to Visual Studio Code, Foundry connects with tools developers love for a frictionless build-to-deploy journey. Start Building Smarter with GPT-5.1-codex-max in Foundry The future is here, and it’s yours to shape. Supercharge your coding workflows with GPT-5.1-codex-max in Microsoft Foundry today. Learn more about Microsoft Foundry: aka.ms/IgniteFoundryModels. Watch Ignite sessions for deep dives and demos: ignite.microsoft.com. Build faster, smarter, and with confidence on the platform redefining enterprise AI.Foundry IQ: Unlocking ubiquitous knowledge for agents
Introducing Foundry IQ by Azure AI Search in Microsoft Foundry. Foundry IQ is a centralized knowledge layer that connects agents to data with the next generation of retrieval-augmented generation (RAG). Foundry IQ includes the following features: Knowledge bases: Available directly in the new Foundry portal, knowledge bases are reusable, topic-centric collections that ground multiple agents and applications through a single API. Automated indexed and federated knowledge sources – Expand what data an agent can reach by connecting to both indexed and remote knowledge sources. For indexed sources, Foundry IQ delivers automatic indexing, vectorization, and enrichment for text, images, and complex documents. Agentic retrieval engine in knowledge bases – A self-reflective query engine that uses AI to plan, select sources, search, rank and synthesize answers across sources with configurable “retrieval reasoning effort.” Enterprise-grade security and governance – Support for document-level access control, alignment with existing permissions models, and options for both indexed and remote data. Foundry IQ is available in public preview through the new Foundry portal and Azure portal with Azure AI Search. Foundry IQ is part of Microsoft's intelligence layer with Fabric IQ and Work IQ.Securing Azure AI Applications: A Deep Dive into Emerging Threats | Part 1
Why AI Security Can’t Be Ignored? Generative AI is rapidly reshaping how enterprises operate—accelerating decision-making, enhancing customer experiences, and powering intelligent automation across critical workflows. But as organizations adopt these capabilities at scale, a new challenge emerges: AI introduces security risks that traditional controls cannot fully address. AI models interpret natural language, rely on vast datasets, and behave dynamically. This flexibility enables innovation—but also creates unpredictable attack surfaces that adversaries are actively exploiting. As AI becomes embedded in business-critical operations, securing these systems is no longer optional—it is essential. The New Reality of AI Security The threat landscape surrounding AI is evolving faster than any previous technology wave. Attackers are no longer focused solely on exploiting infrastructure or APIs; they are targeting the intelligence itself—the model, its prompts, and its underlying data. These AI-specific attack vectors can: Expose sensitive or regulated data Trigger unintended or harmful actions Skew decisions made by AI-driven processes Undermine trust in automated systems As AI becomes deeply integrated into customer journeys, operations, and analytics, the impact of these attacks grows exponentially. Why These Threats Matter? Threats such as prompt manipulation and model tampering go beyond technical issues—they strike at the foundational principles of trustworthy AI. They affect: Confidentiality: Preventing accidental or malicious exposure of sensitive data through manipulated prompts. Integrity: Ensuring outputs remain accurate, unbiased, and free from tampering. Reliability: Maintaining consistent model behavior even when adversaries attempt to deceive or mislead the system. When these pillars are compromised, the consequences extend across the business: Incorrect or harmful AI recommendations Regulatory and compliance violations Damage to customer trust Operational and financial risk In regulated sectors, these threats can also impact audit readiness, risk posture, and long-term credibility. Understanding why these risks matter builds the foundation. In the upcoming blogs, we’ll explore how these threats work and practical steps to mitigate them using Azure AI’s security ecosystem. Why AI Security Remains an Evolving Discipline? Traditional security frameworks—built around identity, network boundaries, and application hardening—do not fully address how AI systems operate. Generative models introduce unique and constantly shifting challenges: Dynamic Model Behavior: Models adapt to context and data, creating a fluid and unpredictable attack surface. Natural Language Interfaces: Prompts are unstructured and expressive, making sanitization inherently difficult. Data-Driven Risks: Training and fine-tuning pipelines can be manipulated, poisoned, or misused. Rapidly Emerging Threats: Attack techniques evolve faster than most defensive mechanisms, requiring continuous learning and adaptation. Microsoft and other industry leaders are responding with robust tools—Azure AI Content Safety, Prompt Shields, Responsible AI Frameworks, encryption, isolation patterns—but technology alone cannot eliminate risk. True resilience requires a combination of tooling, governance, awareness, and proactive operational practices. Let's Build a Culture of Vigilance: AI security is not just a technical requirement—it is a strategic business necessity. Effective protection requires collaboration across: Developers Data and AI engineers Cybersecurity teams Cloud platform teams Leadership and governance functions Security for AI is a shared responsibility. Organizations must cultivate awareness, adopt secure design patterns, and continuously monitor for evolving attack techniques. Building this culture of vigilance is critical for long-term success. Key Takeaways: AI brings transformative value, but it also introduces risks that evolve as quickly as the technology itself. Strengthening your AI security posture requires more than robust tooling—it demands responsible AI practices, strong governance, and proactive monitoring. By combining Azure’s built-in security capabilities with disciplined operational practices, organizations can ensure their AI systems remain secure, compliant, and trustworthy, even as new threats emerge. What’s Next? In future blogs, we’ll explore two of the most important AI threats—Prompt Injection and Model Manipulation—and share actionable strategies to mitigate them using Azure AI’s security capabilities. Stay tuned for practical guidance, real-world scenarios, and Microsoft-backed best practices to keep your AI applications secure. Stay Tuned.!
