Techniques to avoid common LLM pitfalls by Mete Atamel

LLMs have significant pitfalls including hallucinations, reliance on outdated public data, and potentially harmful/chaotic outputs that need careful management

Use frameworks like LangChain and Semantic Kernel to handle pre/post-processing, orchestrate LLM calls, and provide building blocks for LLM applications

Implement Retrieval Augmented Generation (RAG) to ground LLMs with private data through vector databases, reducing hallucinations and providing more accurate responses

Set response types and schemas when calling LLMs to ensure structured, predictable outputs in specific formats (like JSON) rather than free-form text

Leverage context caching and batch processing to reduce costs when making multiple LLM calls

Use evaluation frameworks like DeepEval and PromptFoo to test LLM outputs, measure accuracy, and track improvements

Enable safety settings and implement security measures to prevent harmful content, PII exposure, and prompt injection attacks

Consider using function calling to augment LLMs with external API capabilities and real-time data access

For Google’s ecosystem: Start with Google AI Studio for experimentation, then move to Vertex AI for production deployment

Choose appropriate model versions based on needs - consider context window size, multimodal capabilities, and cost tradeoffs between options like Gemini 1.0 vs 1.5 Pro