LLM Observability: What to Log and Trace in Production AI Features
Quick answer: log every LLM call as a trace with the prompt version, model and parameters, redacted inputs and outputs, retrieved document IDs, tool calls,...
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Fine-Tuning vs RAG vs Prompting: Choosing an Approach for Domain Knowledge
Quick answer: start with prompting, because it is the cheapest to try. Use retrieval-augmented generation when answers depend on private, fresh, or frequently...
Token Budgeting and Context Window Management in Long Conversations
Quick answer: every model has a limited context window, and long conversations eventually exceed it or become expensive. Set a token budget for each part of...
Embedding Model Versioning and Reindexing Strategies for Vector Search
Quick answer: vectors produced by different embedding models, or different versions of one model, live in different spaces and cannot be compared. Store the...
LLM Response Streaming: Server-Sent Events vs WebSockets
Quick answer: for most LLM chat interfaces, Server-Sent Events are the simpler choice. They stream text one way over plain HTTP, reconnect automatically, and...
Choosing Chunk Size and Overlap for RAG Document Ingestion
Quick answer: there is no universal best chunk size. Small chunks retrieve precise passages but lose surrounding context, while large chunks keep context but...
LLM Prompt Versioning and Regression Testing for Production Apps
Quick answer: treat prompts like code. Store them in version control, give each one a version identifier, and run a fixed regression test set against every...
Monitoring LLM Prompt Drift Across Application Releases
Direct answer: Prompt changes can alter extraction or tool decisions without source code modifications. Production decisions about llm prompt drift monitoring...
RAG Context Packing by Relevance vs Document Diversity
Direct answer: Top-ranked passages may all repeat material from one source. Production decisions about rag context packing diversity should be guided by...