AI Engineering & LLM Development (page 2 of 7)
Building LLM apps, agents, RAG, MCP, evals, and production AI systems — guides for engineers shipping real AI products.
- Best Metrics For AI Application Quality
The best AI quality metrics are not generic benchmark scores. They are a compact scorecard of outcome, reliability, safety, latency, and cost signals tied to how the application really succeeds or fails in production.
- Best Practices For Production LLM Applications
A practical guide to the best practices for production LLM applications, from narrow workflow design and structured outputs to evals, guardrails, tracing, fallback paths, and gradual launch controls.
- Best Prompt Patterns For Production AI Apps
A practical guide to the best prompt patterns for production AI apps, from schema-first JSON and retrieval-grounded answers to tool policies, fallback prompts, and eval-driven prompt iteration.
- Building Multi Tool AI Agents
A detailed guide to building multi tool AI agents that can plan, call tools reliably, recover from failures, and operate safely in production.
- Chunking Strategies For RAG Explained
A practical guide to chunking strategies for RAG, including fixed-size, recursive, semantic, hierarchical, and contextual chunking, with production tips for better retrieval quality.
- Common LLM API Errors And How To Fix Them
A practical guide to debugging common LLM API errors in production, from 400s and 401s to rate limits, schema bugs, retries, timeouts, and model deprecations.
- Common RAG Mistakes And How To Fix Them
Common RAG mistakes rarely come from the model alone. This guide explains how to fix broken chunking, weak retrieval, missing reranking, stale indexes, bad prompts, and poor evaluation in production RAG systems.
- Context Window Optimization Explained
Context window optimization is the discipline of giving an LLM the smallest, cleanest, highest-value working set needed to complete a task well. This guide explains token budgeting, retrieval design, memory compression, prompt structure, and production patterns that improve quality while reducing cost and latency.
- The Difference Between Chatbots And AI Agents
Chatbots generate responses inside a conversation. AI agents can reason over tasks, use tools, follow multi-step workflows, and act on external systems. This guide explains the practical difference and helps you choose the right pattern for production.
- Embeddings Explained For LLM Developers
A practical guide to embeddings for LLM developers, covering vector similarity, chunking, metadata, retrieval pipelines, and common production mistakes.