AI Engineering & LLM Development (page 4 of 7)
Building LLM apps, agents, RAG, MCP, evals, and production AI systems — guides for engineers shipping real AI products.
- How To Connect AI Models To External Tools
A practical guide to connecting AI models to external tools, from function schemas and execution loops to MCP servers, approvals, observability, and production hardening.
- How To Debug Tool Calling Failures In LLM Apps
A practical guide to debugging tool calling failures in LLM apps, from wrong tool selection and malformed arguments to silent execution errors, policy failures, and agent trace analysis.
- How To Design A Production Ready LLM System
A practical guide to designing a production ready LLM system from scoping and architecture to evals, guardrails, tracing, latency, cost control, and safe rollout.
- How To Evaluate An LLM App Properly
A practical guide to evaluating an LLM app properly with clear methods for dataset design, automated and human grading, trace-based debugging, regression testing, and production monitoring.
- How To Improve RAG Retrieval Quality
A practical guide to improving RAG retrieval quality, with concrete fixes for chunking, indexing, metadata, hybrid retrieval, reranking, and retrieval evaluation in production.
- How To Move From AI Prototype To Production
A practical guide to moving from AI prototype to production with clear steps for hardening architecture, adding evals, improving reliability, controlling cost, and launching safely.
- How To Reduce Tool Overload In Agentic Systems
A practical guide to reducing tool overload in agentic systems, with clear patterns for smaller tool surfaces, better routing, specialist workflows, and more reliable production agents.
- How To Test AI Agents Systematically
A practical guide to testing AI agents systematically, with clear methods for scenario design, trace-based grading, tool-use evaluation, regression testing, and production monitoring.
- How To Write Better Prompts For LLM Apps
A practical guide to writing better prompts for LLM apps, with production patterns for clarity, grounding, structured outputs, examples, reusable prompt design, and safer iteration.
- Hybrid Search vs Vector Search
A practical guide to hybrid search vs vector search, including exact-match retrieval, semantic retrieval, reranking, and production tradeoffs for real-world knowledge systems.