Top 1% Engineers

Top 1% Engineers

One issue at a time on building AI systems that hold up in production: architecture, the open source that matters, and what to do on Monday. Every number cited.

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Latest

  1. Issue 2How to build a production-ready MCP serverHow to build an MCP server that holds up in production: transport, OAuth audience checks, typed small results, tool design and a person on every write.
  2. Issue 1AI agents vs workflows: make the workflow your defaultAI agents vs workflows is the first call in any AI system. What each choice costs, how it fails, and the code for the safe default.

Guides

  1. Retry patterns for LLM API errors in productionBuild retry logic that handles rate limits, timeouts, and transient failures without burning money. Backoff rules and errors you must not retry.
  2. Fact-checking RAG answers: grounding with verificationFact-check RAG answers with a second LLM pass verifying every claim against retrieved context. Prompt, rejection rule, verification loop.
  3. Retriever k-value tuning for RAG: the right top-kPick the right top-k for your RAG retriever. 3-step tuning process, the failure modes of k=3 and k=20, and the sweet spot in between.
  4. FAISS vector stores in production RAGUse FAISS for production RAG. Index types, persistence, memory trade-offs, and when FAISS beats a managed vector DB like Pinecone or Weaviate.
  5. Langfuse + Grafana: agentic AI monitoringCombine Langfuse traces with Grafana dashboards for production agent monitoring. Integration, panels, alerting, and the workflow that ships.
  6. FastAPI + LangGraph: production chatbot API patternWire LangGraph into a FastAPI chatbot API with streaming, per-user thread persistence, and the production patterns beyond the demo.
  7. Prometheus metrics for agentic AI observabilityInstrument agentic AI with Prometheus. The 4 metrics that matter, histogram trap, counter design, and the dashboard that catches regressions fast.
  8. bash tools for coding agents: safe shell executionBuild a bash tool for coding agents that is safe, scoped, and timeout-bounded. The denylist trap, the allowlist fix, and the sandbox pattern.
  9. LangChain chain types: stuff vs map reduce vs refineStuff vs Map Reduce vs Refine in LangChain. Pick the right chain type for RAG summarization based on document size, cost, and quality trade-offs.
  10. LangGraph persistence: thread models for production agentsHow LangGraph persistence and the postgres checkpointer keep agent state across restarts. Thread models, race conditions, and what to wire on day one.
  11. .env.development vs .env: config for agentic AI systemsWhy your agent needs .env.development and .env, not one file. Pydantic settings, secret loading order, and the leaks this prevents in production.
  12. FastAPI and Uvicorn for production agentic AI systemsFastAPI and Uvicorn for agentic AI: ASGI workers, streaming, lifespan, and the production gotchas tutorials skip. Build agents that survive real traffic.
  13. System design for production AI chatbotsMaster end-to-end system design for production AI chatbots. Learn streaming architecture, memory management, RAG integration, rate limiting, circuit breake
  14. Multi-hop RAG: when one retrieval isn't enoughLearn how to build multi-hop RAG agents that can decompose complex queries into sub-questions and chain multiple retrieval steps. Understand query decompos
  15. Giving your LLM hands: a deep dive on tool callingLearn how to build agents that can interact with the real world using tool calling. Step-by-step guide to implementing function calling with Pydantic, Open
  16. Batteries-included RAG platforms: Dify vs. RagFlow vs. OnyxCompare batteries-included RAG platforms that bundle the entire pipeline into one application. Learn when to use Dify, RAGFlow, and Onyx for rapid deployme
  17. Choosing your RAG framework: LangChain vs. LlamaIndex vs. HaystackDemystify RAG frameworks by understanding their core philosophies. Learn when to use LangChain, LlamaIndex, and Haystack for building production-ready retr
  18. Self-critique and refinement promptingLearn how to improve LLM output quality using Self-Critique and Refinement techniques. Force LLMs to review and improve their own work through iterative re