OptiMind: A small language model with optimization expertise
Turning a real world decision problem into a solver ready optimization model can take days—sometimes weeks—even for experienced teams. The hardest part is often not solving the problem; it’s translating business intent into precise mathematical objectives, constraints, and variables. OptiMind is designed to try and remove that bottleneck. This optimization‑aware language model translates natural‑language problem descriptions into solver‑ready mathematical formulations, can help organizations move from ideas to decisions faster. Now available through public preview as an experimental model through Microsoft Foundry, OptiMind targets one of the more expertise‑intensive steps in modern optimization workflows. Addressing the Optimization Bottleneck Mathematical optimization underpins many enterprise‑critical decisions—from designing supply chains and scheduling workforces to structuring financial portfolios and deploying networks. While today’s solvers can handle enormous and complex problem instances, formulating those problems remains a major obstacle. Defining objectives, constraints, and decision variables is an expertise‑driven process that often takes days or weeks, even when the underlying business problem is well understood. OptiMind tries to address this gap by automating and accelerating formulation. Developed by Microsoft Research, OptiMind transforms what was once a slow, error‑prone modeling task into a streamlined, repeatable step—freeing teams to focus on decision quality rather than syntax. What makes OptiMind different? OptiMind is not just as a language model, but as a specialized system built for real-world optimization tasks. Unlike general-purpose large language models adapted for optimization through prompting, OptiMind is purpose-built for mixed integer linear programming (MILP), and its design reflects this singular focus. At inference time, OptiMind follows a multi‑stage process: Problem classification (e.g., scheduling, routing, network design) Hint retrieval tailored to the identified problem class Solution generation in solver‑compatible formats such as GurobiPy Optional self‑correction, where multiple candidate formulations are generated and validated This design can improve reliability without relying on agentic orchestration or multiple large models. In internal evaluations on cleaned public benchmarks—including IndustryOR, Mamo‑Complex, and OptMATH—OptiMind demonstrated higher formulation accuracy than similarly sized open models and competitive performance relative to significantly larger systems. OptiMind improved accuracy by approximately 10 percent over the base model. In comparison to open-source models under 32 billion parameters, OptiMind was also found to match or exceed performance benchmarks. For more information on the model, please read the official research blog or the technical paper for OptiMind. Practical use cases: Unlocking efficiency across domains OptiMind is especially valuable where modeling effort—not solver capability—is the primary bottleneck. Typical use cases include: Supply Chain Network Design: Faster formulation of multi‑period network models and logistics flows Manufacturing and Workforce Scheduling: Easier capacity planning under complex operational constraints Logistics and Routing Optimization: Rapid modeling that captures real‑world constraints and variability Financial Portfolio Optimization: More efficient exploration of portfolios under regulatory and market constraints By reducing the time and expertise required to move from problem description to validated model, OptiMind helps teams reach actionable decisions faster and with greater confidence. Getting started OptiMind is available today as an experimental model, and Microsoft Research welcomes feedback from practitioners and enterprise teams. Next steps: Explore the research details: Read more about the model on Foundry Labs and the technical paper on arXiv Try the model: Access OptiMind through Microsoft Foundry Test sample code: Available in the OptiMind GitHub repository Take the next step in optimization innovation with OptiMind—empowering faster, more accurate, and cost-effective problem solving for the future of decision intelligence.The Future of AI: From Noise to Insight - An AI Agent for Customer Feedback
This post explores how Microsoft’s AI Futures team built a multi-agent system to transform scattered customer feedback into actionable insights. The solution aggregates feedback from multiple channels, uses advanced language models to cluster themes, summarize content, and identify sentiment, and delivers prioritized insights directly in Microsoft Teams. With human-in-the-loop safeguards, the system accelerates triage, prioritization, and follow-ups while maintaining compliance and traceability. Future enhancements include richer automation, trend visualization, and expanded feedback sources.The Future of AI: Structured Vibe Coding - An Improved Approach to AI Software Development
In this post from The Future of AI series, the author introduces structured vibe coding, a method for managing AI agents like a software team using specs, GitHub issues, and pull requests. By applying this approach with GitHub Copilot, they automated a repetitive task—answering Microsoft Excel-based questionnaires—while demonstrating how AI can enhance developer workflows without replacing human oversight. The result is a scalable, collaborative model for AI-assisted software development.The Future of AI: Harnessing AI agents for Customer Engagements
Discover how AI-powered agents are revolutionizing customer engagement—enhancing real-time support, automating workflows, and empowering human professionals with intelligent orchestration. Explore the future of AI-driven service, including Customer Assist created with Azure AI Foundry.
Announcing the Text PII August preview model release in Azure AI language
Azure AI Language is excited to announce a new preview model release for the PII (Personally Identifiable Information) redaction service, which includes support for more entities and languages, addressing customer-sourced scenarios and international use cases. What’s New | Updated Model 2025-08-01-preview Tier 1 language support for DateOfBirth entity: expanding upon the original English-only support earlier this year, we’ve added support for all Tier 1 languages: French, German, Italian, Spanish, Portuguese, Brazilian Portuguese, and Dutch New entity support: SortCode - a financial code used in the UK and Ireland to identify the specific bank and branch where an account is held. Currently we support this in only English. LicensePlateNumber - the standard alphanumeric code for vehicle identification. Note that our current scope does not support a license plate that contains only letters. Currently we support this in only English. AI quality improvements for financial entities, reducing false positives/negatives These updates respond directly to customer feedback and address gaps in entity coverage and language support. The broader language support enables global deployments and the new entity types allow for more comprehensive data extraction for our customers. This ensures an improved service quality for financial, criminal justice, and many other regulatory use cases, enabling more accurate and reliable service for our customers. Get started A more detailed tutorial and overview of the service feature can be found in our public docs. Learn more about these releases and several others enhancing our Azure AI Language offerings on our What’s new page. Explore Azure AI Language and its various capabilities Access full pricing details on the Language Pricing page Find the list of sensitive PII entities supported Try out Azure AI Foundry for a code-free experience We are looking forward to continuously improving our product offerings and features to meet customer needs and are keen to hear any comments and feedback.Project Maria: Bringing Speech and Avatars Together for Next-Generation Customer Experiences
In an age where digital transformation influences nearly every aspect of business, companies are actively seeking innovative ways to differentiate their customer interactions. Traditional text-based chatbots, while helpful, often leave users wanting a more natural, personalized, and efficient experience. Imagine hosting a virtual brand ambassador—a digital twin of yourself or your organization’s spokesperson—capable of answering customer queries in real time with a lifelike voice and expressive 2D or 3D face. This is where Project Maria comes in. Project Maria is an internal Microsoft initiative that integrates cutting-edge speech-to-text (STT), text-to-speech (TTS), large language model and avatar technologies. Using Azure AI speech and custom neural voice models, it seeks to create immersive, personalized interactions for customers—reducing friction, increasing brand loyalty, and opening new business opportunities in areas such as customer support, product briefings, digital twins, live marketing events, safety briefings, and beyond. In this blog post, we will dive into: The Problem and Rationale for evolving beyond basic text-based solutions. Speech-to-Text (STT), Text-to-Speech (TTS) Pipelines, Azure OpenAI GPT-4o Real-Time API that power natural conversations. Avatar Models in Azure, including off-the-shelf 2D avatars and fully customized custom avatar Neural Voice Model Creation, from data gathering to training and deployment on Azure. Security and Compliance considerations for handling sensitive voice assets and data. Use Cases from customer support to digital brand ambassadors and safety briefings. Real-World Debut of Project Maria, showcased at the AI Leaders’ Summit in Seattle. Future Outlook on how custom avatar will reshape business interactions, scale presence, and streamline time-consuming tasks. If you’re developing or considering a neural (custom) voice + avatar models for your product or enterprise, this post will guide you through both conceptual and technical details to help you get started—and highlight where the field is heading next. 1. The Problem: Limitations of Text-Based Chatbots 1.1 Boredom and Fatigue in Text Interactions Text-based chatbots have come a long way, especially with the advent of powerful Large Language Models (LLMs) and Small Large Models (SLMs). Despite these innovations, interactions can still become tedious—often requiring users to spend significant personal time crafting the right questions. Many of us have experienced chatbots that respond with excessively verbose or repetitive messages, leading to boredom or even frustration. In industries that demand immediacy—like healthcare, finance, or real-time consumer support—purely text-based exchanges can feel slow and cumbersome. Moreover, text chat requires a user’s full attention to read and type, whether in a busy contact center environment or an internal knowledge base where employees juggle multiple tasks. 1.2 Desire for More Engaging and Efficient Modalities Today’s users expect something closer to human conversation. Devices ranging from smartphones to smart speakers and in-car infotainment systems have normalized voice-based interfaces. Adding an avatar—whether a 2D or 3D representation—deepens engagement by combining speech with a friendly visual persona. This can elevate brand identity: an avatar that looks, talks, and gestures like your company’s brand ambassador or a well-known subject-matter expert. 1.3 The Need for Scalability In a busy customer support environment, human representatives simply can’t handle an infinite volume of conversations or offer 24/7 coverage across multiple channels. Automation is essential, yet providing high-quality automated interactions remains challenging. While a text-based chatbot might handle routine queries, a voice-based, avatar-enabled agent can manage more complex requests with greater dynamism and personality. By giving your digital support assistant both a “face” and a voice aligned with your brand, you can foster deeper emotional connections and provide a more genuine, empathetic experience. This blend of automation and personalization scales your support operations, ensuring higher customer satisfaction while freeing human agents to focus on critical or specialized tasks. 2. The Vision: Project Maria’s Approach Project Maria addresses these challenges by creating a unified pipeline that supports: Speech-to-Text (STT) for recognizing user queries quickly and accurately. Natural Language Understanding (NLU) layers (potentially leveraging Azure OpenAI or other large language models) for comprehensive query interpretation. Text-to-Speech (TTS) that returns highly natural-sounding responses, possibly in multiple languages, with customized prosody and style. Avatar Rendering, which can be a 2D animated avatar or a more advanced 3D digital twin, bringing personality and facial expressions to the conversation. By using Azure AI Services—particularly the Speech and Custom Neural Voice offerings—can deliver brand-specific voices. This ensures that each brand or individual user’s avatar can match (or approximate) a signature voice, turning a run-of-the-mill voice assistant into a truly personal digital replicas 3. Technical Foundations 3.1 Speech-to-Text (STT) At the heart of the system is Azure AI Services for Speech, which provides: Real-time transcription capabilities with a variety of languages and dialects. Noise suppression, ensuring robust performance in busy environments. Streaming APIs, critical for real-time or near-real-time interactions. When a user speaks, audio data is captured (for example, via a web microphone feed or a phone line) and streamed to the Azure service. The recognized text is returned in segments, which the NLU or conversation manager can interpret. 3.1.1 Audio Pipeline Capture: The user’s microphone audio is captured by a front-end (e.g., a web app, mobile app, or IoT device). Pre-processing: Noise reduction or volume normalization might be applied locally or in the cloud, ensuring consistent input. Azure STT Ingestion: Data is sent to the Speech service endpoint, authenticated via subscription keys or tokens (more on security later). Result Handling: The recognized text arrives in partial hypotheses (partial transcripts) and final recognized segments. Project Maria (Custom Avatar) processes these results to understand user intent 3.2 Text-to-Speech (TTS) Once an intent is identified and a response is formulated, the system needs to deliver speech output. Standard Neural Voices: Microsoft provides a wide range of prebuilt voices in multiple languages. Custom Neural Voice: For an even more personalized experience, you can train a voice model that matches a brand spokesperson or a distinct voice identity. This is done using your custom datasets, ensuring the final system speaks exactly like the recorded persona. 3.2.1 Voice Font Selection and Configuration In a typical architecture: The conversation manager (which could be an orchestrator or a custom microservice) provides the text output to the TTS service. The TTS service uses a configured voice font—like en-US-JennyNeural or a custom neural voice ID (like Maria Neural Voice) if you have a specialized voice model. The synthesized audio is returned as an audio stream (e.g., PCM or MP3). You can play this in a webpage directly or in a native app environment. Azure OpenAI GPT-4o Real-Time API integrates with Azure's Speech Services to enable seamless interactions. First, your speech is transcribed in near real time. GPT-4o then processes this text to generate context-aware responses, which are converted to natural-sounding audio via Azure TTS. This audio is synchronized with avatar models to create a lifelike, engaging interface 3.3 Real-Time Conversational Loop Maria is designed for real-time or text to speech conversations. The user’s speech is continuously streamed to Azure STT. The recognized text triggers a real-time inference step for the next best action or response. The response is generated by Azure OpenAI model (like GPT-4o) or other LLM/SLM The text is then synthesized to speech, which the user hears with minimal latency. 3.4 Avatars: 2D and Beyond 3.4.1 Prebuilt Azure 2D Avatars Azure AI Speech Services includes an Avatar capability that can be activated to display a talking head or a 2D animated character. Developers can: Choose from prebuilt characters or import basic custom animations. Synchronize lip movements to the TTS output. Overlay brand-specific backgrounds or adopt transparency for embedding in various UIs. 3.4.2 Fully Custom Avatars (Customer Support Agent Like Maria) For organizations wanting a customer support agent, subject-matter expert, or brand ambassador: Capture: Record high-fidelity audio and video of the person you want to replicate. The more data, the better the outcome (though privacy and licensing must be considered). Modeling: Use advanced 3D or specialized 2D animation software (or partner with Microsoft’s custom avatar creation solutions) to generate a rigged model that matches the real person’s facial geometry and expressions. Integration: Once the model is rigged, it can be integrated with the TTS engine. As text is converted to speech, the avatar automatically animates lip shapes and facial expressions in near real time. 3.5 Latency and Bandwidth Considerations When building an interactive system, keep an eye on: Network latency: Real-time STT and TTS require stable, fast connections. Compute resources: If hosting advanced ML or high concurrency, scaling containers (e.g., via Docker and Kubernetes) is critical. Avatars: Real-time animation might require sending frames or instructions to a client’s browser or device. 4. Building the Model: Neural Voice Model Creation 4.1 Data Gathering To train a custom neural voice, you typically need: High-quality audio clips: Ideally recorded in a professional studio to minimize background noise, with the same microphone setup throughout. Matching transcripts for each clip. Minimum data duration: Microsoft recommends a certain threshold (e.g., 300+ utterances, typically around 30 minutes to a few hours of recorded speech, depending on the complexity of the final voice needed). 4.2 Training Process Data Upload: Use the Azure Speech portal or APIs to upload your curated dataset. Model Training: Azure runs training jobs that often require a few hours (or more). This step includes: Acoustic feature extraction (spectrogram analysis). Language or phoneme modeling for the relevant language and accent. Prosody tuning, ensuring the voice can handle various styles (cheerful, empathetic, urgent, etc.). Quality Checks: After training, you receive an initial voice model. You can generate test phrases to assess clarity, intonation, and overall quality. Iteration: If the voice quality is not satisfactory, you gather more data or refine the existing data (removing noisy segments or inaccurate transcripts). 4.3 Deployment Once satisfied with the custom neural voice: Deploy the model to an Azure endpoint within your subscription. Configure your TTS engine to use the custom endpoint ID instead of a standard voice. 5. Securing Avatar and Voice Models Security is paramount when personal data, brand identity, or intellectual property is on the line. 5.1 API Keys and Endpoints Azure AI Services requires an API key or an OAuth token to access STT/TTS features. Store keys in Azure Key Vault or as secure environment variables. Avoid hard-coding them in the front-end or source control. 5.2 Access Control Role-Based Access Control (RBAC) at both Azure subscription level and container (e.g., Docker or Kubernetes) level ensures only authorized personnel can deploy or manage the containers running these services. Network Security: Use private endpoints if you want to limit exposure to the public internet. 5.3 Intellectual Property Concerns Avatar and Voice Imitation: A avatar model and custom neural voice that mimics a specific individual must be authorized by that individual. Azure has a verification process in place to ensure consent. Data Storage: The training audio data and transcripts must be securely stored, often with encryption at rest and in transit. 6. Use Cases: Bringing It All Together 6.1 Customer Support A digital avatar that greets users on a website or mobile app can handle first-level queries: “Where can I find my billing information?” “What is your return policy?” By speaking these answers aloud with a friendly face and voice, the experience is more memorable and can reduce queue times for human agents. If the question is too complex, the avatar can seamlessly hand off to a live agent. Meanwhile, transcripts of the entire conversation are stored (e.g., in Azure Cosmos DB), enabling data analytics and further improvements to the system. 6.2 Safety Briefings and Public Announcements Industries like manufacturing, aviation, or construction must repeatedly deliver consistent safety messages. A personal avatar can recite crucial safety protocols in multiple languages, ensuring nothing is lost in translation. Because the TTS voice is consistent, workers become accustomed to the avatar’s instructions. Over time, you could even create a brand or site-specific “Safety Officer” avatar that fosters familiarity. 6.3 Digital Twins at Live Events Suppose you want your company’s spokesperson to simultaneously appear at multiple events across the globe. With a digital twin: The spokesperson’s avatar and voice “present” in real time, responding to local audience questions. This can be done in multiple languages, bridging communication barriers instantaneously. Attendees get a sense of personal interaction, while the real spokesperson can focus on core tasks, or appear physically at another event entirely. 6.4 AI Training and Education In e-learning platforms, a digital tutor can guide students through lessons, answer questions in real time, and adapt the tone of voice based on the difficulty of the topic or the student’s performance. By offering a face and voice, the tutor becomes more engaging than a text-only system. 7. Debut: Maria at the AI Leaders Summit in Seattle Project Maria had its first major showcase at the AI Leaders Summit in Seattle last week. We set up a live demonstration: Live Conversations: Attendees approached a large screen that displayed Maria’s 2D avatar. On-the-Fly: Maria recognized queries with STT, generated text responses from an internal knowledge base (powered by GPT-4o or domain-specific models), then spoke them back with a custom Azure neural voice. Interactive: The avatar lip-synced to the output speech, included animated gestures for emphasis, and even displayed text-based subtitles for clarity. The response was overwhelmingly positive. Customers praised the fluid voice quality and the lifelike nature of Maria’s avatar. Many commented that they felt they were interacting with a real brand ambassador, especially because the chosen custom neural voice had just the right inflections and emotional range. 8. Technical Implementation Details Below is a high-level architecture of how Project Maria might be deployed using containers and Azure resources. Front-End Web App: Built with a modern JavaScript framework (React, Vue, Angular, etc.). Captures user audio through the browser’s WebRTC or MediaStream APIs. Connects via WebSockets or RESTful endpoints for STT requests. Renders the avatar in a <canvas> element or using a specialized avatar library. Backend: Containerized with Docker. Exposes endpoints for STT streaming (optionally passing data directly to Azure for transcription). Integrates with the TTS service, retrieving synthesized audio buffers. Returns the audio back to the front-end in a continuous stream for immediate playback. Avatar Integration: The back-end or a specialized service handles lip-sync generation (e.g., via phoneme mapping from the TTS output). The front-end renders the 2D or 3D avatar in sync with the audio playback. This can be done by streaming timing markers that indicate which phoneme is currently active. Data and Conversation Storage: Use an Azure Cosmos DB or a similar NoSQL solution to store transcripts, user IDs, timestamps, and optional metadata (e.g., conversation sentiment). This data can later be used to improve the conversation model, evaluate performance, or train advanced analytics solutions. Security: All sensitive environment variables (like Azure API keys) are loaded securely, either through Azure Key Vault or container orchestration secrets. The system enforces user authentication if needed. For instance, an internal HR system might restrict the avatar-based service to employees only. Scaling: Deploy containers in Azure Kubernetes Service (AKS), setting up auto-scaling to handle peak loads. Monitor CPU/memory usage, as well as TTS quota usage. For STT, ensure the service tier can handle simultaneous requests from multiple users. 9. Securing Avatar Models and Voice Data 9.1 Identity Management Each avatar or custom neural voice is tied to a specific subscription. Using Azure Active Directory (Azure AD), you can give fine-grained permissions so that only authorized DevOps or AI specialists can alter or redeploy the voice. 9.2 API Gateways and Firewalls For enterprise contexts, you might place an API Gateway in front of your containerized services. This central gateway can: Inspect requests for anomalies, Enforce rate-limits, Log traffic to meet compliance or auditing requirements. 9.3 Key Rotation and Secrets Management Frequently rotates keys to minimize the risk of compromised credentials. Tools like Azure Key Vault or GitHub’s secret storage features can automate the rotation process, ensuring minimal downtime. 10. The Path Forward: Scaling Custom Avatar 10.1 Extended Personalization While Project Maria currently focuses on voice and basic facial expressions, future expansions include: Emotion Synthesis: Beyond standard TTS expressions (friendly, sad, excited), we can integrate emotional AI to dynamically adjust the avatar’s tone based on user sentiment. Gesture Libraries: 2D or 3D avatars can incorporate hand gestures, posture changes, or background movements to mimic a real person in conversation. This reduces the “uncanny valley” effect. 10.2 Multilingual, Multimodal As businesses operate globally, multilingual interactions become paramount. We have seen many use cases to: Auto-detect language from a user’s speech and respond in kind. Offer real-time translation, bridging non-English speakers to brand content. 10.3 Agent Autonomy Systems like Maria won’t just respond to direct questions; they can initiate proactivity: Send voice-based notifications or warnings when critical events happen. Manage long-running tasks such as scheduling or triaging user requests, akin to an “executive assistant” for multiple users simultaneously. 10.4 Ethical and Social Considerations With near-perfect replicas of voices, there is a growing concern about identity theft, misinformation, and deepfakes. Companies implementing digital twins must: Secure explicit consent from individuals. Implement watermarking or authentication for voice data. Educate customers and employees on usage boundaries and disclaimers 11. Conclusion Project Maria represents a significant leap in how businesses and organizations can scale their presence, offering a humanized, voice-enabled digital experience. By merging speech-to-text, text-to-speech, and avatar technologies, you can: Boost Engagement: A friendly face and familiar voice can reduce user fatigue and build emotional resonance. Extend Brand Reach: Appear in many locations at once via digital twins, creating personalized interactions at scale. Streamline Operations: Automate repetitive queries while maintaining a human touch, freeing up valuable employee time. Ensure Security and Compliance: By using Azure’s robust ecosystem of services and best practices for voice data. As demonstrated at the AI Leaders Summit in Seattle, Maria is already reshaping how businesses think about communication. The synergy of avatars, neural voices, and secure, cloud-based AI is paving the way for the next frontier in customer interaction. Looking ahead, we anticipate that digital twins—like Maria—will become ubiquitous, automating not just chat responses but a wide range of tasks that once demanded human presence. From personalized marketing to advanced training scenarios, the possibilities are vast. In short, the fusion of STT, TTS, and avatar technologies is more than a novel gimmick; it is an evolution in human-computer interaction. By investing in robust pipelines, custom neural voice training, and carefully orchestrated containerized deployments, businesses can unlock extraordinary potential. Project Maria is our blueprint for how to do it right—secure, customizable, and scalable—helping organizations around the world transform user experiences in ways that are both convenient and captivating. If you’re looking to scale your brand, innovate in human-machine dialogues, or harness the power of digital twins, we encourage you to explore Azure AI Services’ STT, TTS, and Avatar solutions. Together, these advancements promise a future where your digital self (or brand persona) can meaningfully interact with users anytime, anywhere. Detailed Technical Implementation:- https://learn.microsoft.com/en-us/azure/ai-services/speech-service/text-to-speech-avatar/what-is-custom-text-to-speech-avatar Text to Speech with Multi-Agent Orchestration Framework:- https://github.com/ganachan/Project_Maria_Accelerator_tts Contoso_Maria_Greetings.mp4Document Redaction and Sanitization with ChatGPT on Azure
Privacy and Security are the topmost priorities for Consumers and Businesses. Bad actors can steal the information when it is in transit between the systems, when it is being processed or even when archived. Large Language Models have opened doors for more effective ways of sanitizing the documents. In this article I will provide some examples of how to sanitize the documents using ChatGPT on Azure. What is Document Redaction and Sanitization? It is the process of removing or replacing any information that is considered as sensitive, private or confidential. What types of information needs redaction? Following are some sensitive information types that needs redaction: Personally Identifiable and Information (PII): Anything information that helps identify a person called needs to be redacted. Examples include people's names, addresses, social security numbers, drivers license etc. Protected Health Information(PHI) : Health related information such as patient’s medical records, insurance group numbers, benefit information etc. Business Confidential Information: Organizational information such as employee records, biometric records, business related secrets, contractual information, financial documents, judicial records etc. Using ChatGPT on Azure for Redacting and Sanitizing the documents: Example 1: Sanitize the sensitive information from an Invoice The sample invoice I am going to use is in scanned format. As ChatGPT can only take plain text as input currently, we first have to digitalize the invoice. Form Recognizer service is a very effective way of digitizing the scanned documents. Below is a screenshot from Form Recognizer with the Invoice example: After extracting the data from the invoice using Form Recognizer and some cleansing, we will have the plain text something like below: You will find the responses below from ChatGPT for my redaction and sanitizing instructions: Prompt: Show me all the references of PII below: Prompt: redact all references of PII data below: Prompt: Replace all occurrences of Contoso with LinkedIn: Prompt: convert all dates to MON-DD-YYYY format. show me only dates: Example 2: Sanitize information from a Health Insurance Card for PHI data: Similar to the above example, I used the Form Recognizer service to extract the information from a health insurance card. Below are some examples on how to sanitize the PHI information. Prompt: show me all references of PHI: Prompt: redact all references of people names below: You can continue with various redaction and sanitization activities in the document such as replacing text, removing text, translating text, converting currencies etc. All the Best! Note 1: The responses may vary depending on the hyperparameters like Temperature, Top Probabilities etc. Note 2: I also encourage you all to try the same prompts on text-davinci-003 model also.
