/2 min
Why I don't use LangChain in production RAG
Frameworks are great for a demo. In production, the abstraction you can't see into costs more than it saves.
Topic
LLM App Architecture
Prompting, context management, streaming, and structured output for real applications.
/3 min
When NOT to use RAG (RAG vs fine-tuning vs prompting)
RAG became the default reflex for every LLM problem. Often it's the wrong tool. A straight decision framework for RAG vs fine-tuning vs just prompting.
/3 min
Streaming LLM Responses in FastAPI: SSE, Backpressure, and the Latency Traps Nobody Warns You About
Stream LLM tokens in FastAPI with SSE the moment they arrive, kill buffering layers, and cancel on disconnect so you stop paying for unseen tokens.
/4 min/reference
Self-RAG vs Corrective RAG (CRAG): Which Self-Correcting Pattern Should You Use?
CRAG fixes retrieval quality before generation; Self-RAG uses fine-tuned reflection tokens to critique its own output. Here's when to use each.
/4 min/reference
How to Get Reliable JSON From LLMs: JSON Mode vs Structured Outputs vs Constrained Decoding
Structured Outputs with grammar-constrained decoding is the most reliable way to get valid JSON from an LLM. Here's how the three mechanisms compare.
/3 min/reference
Prompt Caching Explained: How to Cut LLM Cost by up to 90% Without Losing Quality
Prompt caching reuses precomputed KV state for identical prompt prefixes, cutting cost up to 90% and latency up to 85% with no quality change.
/2 min/reference
A reference architecture for production RAG
The stages every production RAG system needs — indexing, query-time retrieval, and always-on evaluation — and how to order them.
/4 min/reference
LLM Fallback Strategies: Building Multi-Model Routing That Survives Outages and Rate Limits
Build a production LLM fallback strategy: a cost-ordered chain of models with health checks, backoff, and cross-provider routing.