Best Practices for Mitigating Hallucinations in Large Language Models (LLMs)

Real-world AI Solutions: Lessons from the Field

Overview 

This document provides practical guidance for minimizing hallucinations—instances where models produce inaccurate or fabricated content—when building applications with Azure AI services. It targets developers, architects, and MLOps teams working with LLMs in enterprise settings.

Key Outcomes

✅ Reduce hallucinations through retrieval-augmented strategies and prompt engineering
✅ Improve model output reliability, grounding, and explainability
✅ Enable robust enterprise deployment through layered safety, monitoring, and security

Understanding Hallucinations

Hallucinations come in different forms. Here are some realistic examples for each category to help clarify them:

Mitigation Strategies

1- Retrieval-Augmented Generation (RAG)

Grounding model outputs with enterprise knowledge sources like PDFs, SharePoint docs, or images.

Key Practices:

• Data Preparation and Organization
  • Clean and curate your data.
  • Organize data into topics to improve search accuracy and prevent noise.
  • Regularly audit and update grounding data to avoid outdated or biased content.
• Search and Retrieval Techniques
  • Explore different methods (keyword, vector, hybrid, semantic search) to find the best fit for your use case.
  • Use metadata filtering (e.g., tagging by recency or source reliability) to prioritize high-quality information.
  • Apply data chunking to improve retrieval efficiency and clarity.
• Query Engineering and Post-Processing
  • Use prompt engineering to specify which data source or section to pull from.
  • Apply query transformation methods (e.g., sub-queries) for complex queries.
  • Employ reranking methods to boost output quality.

2- Prompt Engineering

High-quality prompts guide LLMs to produce factual and relevant responses. Use the ICE method:

• Instructions: Start with direct, specific asks.
• Constraints: Add boundaries like "only from retrieved docs".
• Escalation: Include fallback behaviors (e.g., “Say ‘I don’t know’ if unsure”).

Example Prompt Improvement:

❌: Summarize this document. 

✅: Using only the retrieved documentation, summarize this paper in 3–5 bullet points. If any information is missing, reply with 'Insufficient data.'

Prompt Patterns That Work:

• Clarity and Specificity
  • Write clear, unambiguous instructions to minimize misinterpretation.
  • Use detailed prompts, e.g., "Provide only factual, verified information. If unsure, respond with 'I don't know.'"
• Structure
  • Break down complex tasks into smaller logical subtasks for accuracy. Example: Research Paper Analysis

❌ Bad Prompt (Too Broad, Prone to Hallucination): "Summarize this research paper and explain its implications."

✅ Better Prompt (Broken into Subtasks):

• • • • Extract Core Information: "Summarize the key findings of the research paper in 3-5 bullet points."
      • Assess Reliability: "Identify the sources of data used and assess their credibility."
      • Determine Implications: "Based on the findings, explain potential real-world applications."
      • Limit Speculation: "If any conclusions are uncertain, indicate that explicitly rather than making assumptions."
• Repetition
  • Repeating key instructions in a prompt can help reduce hallucinations. The way you structure the repetition matters. Here are some best practices:
• Beginning (Highly Recommended)
  • The start of the prompt has the most impact on how the LLM interprets the task. Place essential guidelines here, such as: "Provide only factual, verified information."
• End (For Final Confirmation or Safety Checks)
  • Use the end to reinforce key rules. Instead of repeating the initial instruction verbatim, word it differently to reinforce it, and keep it concise. For example: "If unsure, clearly state 'I don't know.'"
• Temperature Control
  • Adjust temperature settings (0.1–0.4) for deterministic, focused responses.
• Chain-of-Thought
  • Incorporate "Chain-of-Thought" instructions to encourage logical, stepwise responses. For example, to solve a math problem: "Solve this problem step-by-step. First, break it into smaller parts. Explain each step before moving to the next."

 Tip: Use Azure AI Prompt Flow’s playground to test prompt variations with parameter sweeps.

3- System-Level Defenses

Mitigation isn't just prompt-side—it requires end-to-end design.

Key Recommendations:

• Content Filtering: Use Azure AI Content Safety to detect sexual, hate, violence, or self-harm content.
• Metaprompts: Define system boundaries ("You can only answer from documents retrieved").
• RBAC & Networking: Use Azure Private Link, VNETs, and Microsoft Entra ID for secure access.

4- Evaluation & Feedback Loops

Continuously evaluate outputs using both automated and human-in-the-loop feedback.

Real-World Setup:

• Labeling Teams: Review hallucination-prone cases with Human in Loop integrations.
• Automated Test Generation

• • Use LLMs to generate diverse test cases covering multiple inputs and difficulty levels.
  • Simulate real-world queries to evaluate model accuracy.

• Evaluations Using Multiple LLMs

• Cross-evaluate outputs from multiple LLMs.
• Use ranking and comparison to refine model performance.
• Be cautious—automated evaluations may miss subtle errors requiring human oversight.

Tip: Common Evaluation Metrics

Tip: Use evaluation scores in combination. For example, high relevance but low groundedness often signals hallucination risks—especially in chat apps with fallback answers.

Tip: Flag any outputs where "source_confidence" < threshold and route them to a human review queue.

Tip: Include “accuracy audits” as part of your CI/CD pipeline using Prompt Flow or other evaluations tools to test components.

Summary & Deployment Checklist

Additional AI Best Practices blog posts:

Best Practices for Requesting Quota Increase for Azure OpenAI Models

Best Practices for Leveraging Azure OpenAI in Constrained Optimization Scenarios

Best Practices for Structured Extraction from Documents Using Azure OpenAI

Best Practices for Using Generative AI in Automated Response Generation for Complex Decision Making

Best Practices for Leveraging Azure OpenAI in Code Conversion Scenarios

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