Make ChatGPT Reliable - Avoiding Hallucinations and Building Stable LLM APIs

Overview

Discover hard-won strategies for building reliable LLM APIs, avoiding hallucinations, enforcing consistency, and transforming any prompt into a stable, production-ready function.

Syllabus

- Introduction to LLM Reliability -- Overview of Language Model Limitations -- Importance of Reducing Hallucinations - Understanding and Identifying Hallucinations -- Defining Hallucinations in Language Models -- Techniques to Detect Hallucinations -- Case Studies and Examples - Strategies for Avoiding Hallucinations -- Crafting Effective Prompts -- Implementing Feedback Loops for Correction -- Using External Validation Sources - Building Stable LLM APIs -- Best Practices for API Design -- Ensuring Consistency in Outputs -- Version Control and Rollback Strategies - Enforcing Consistency -- Techniques for Maintaining Uniformity -- Leveraging Templates and Structured Outputs -- Role of Regular Expressions and Constraints - Transforming Prompts into Production-Ready Functions -- Prompt Engineering for Reliability -- Integrating Error Handling Mechanisms -- Real-world Examples and Success Stories - Testing and Evaluation -- Setting Up Robust Testing Frameworks -- Balancing Performance with Reliability -- Analyzing Model Outputs and Metrics - Future Trends and Developments -- Emerging Techniques for Improved Reliability -- The Role of Community and Collaboration Tools - Capstone Project -- Design and Deploy a Reliable LLM API -- Apply Strategies to Minimize Hallucinations -- Present Findings and Lessons Learned

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