AI Engineering Blog
Practical guides for developers building with LLMs, RAG, and agentic AI.
Prompt Engineering: 17 Techniques That Fix Bad LLM Output (2026)
LLM outputs still vague after tweaking temperature? These 17 techniques — CoT, few-shot, RAG prompting.
RAG Evaluation: Stop Hallucinations Before Production (2026)
RAG pipeline shipping wrong answers? RAGAS catches them — faithfulness, relevancy, context precision and recall measured with copy-paste Python code. Includes CI/CD integration.
Open Source LLMs: Pick the GGUF Model for Your GPU (2026)
Downloaded the wrong GGUF and it crashed your RAM? Compare Llama 3.2, Phi-4, and Mistral by size, benchmark, and hardware fit — before you pull.
LoRA Fine-Tuning: Cut GPU Memory 10x, Keep Quality (2026)
Full fine-tuning blowing your GPU budget? LoRA slashes memory 10x while matching quality — learn how with Python examples and real benchmarks.
Multi-Document RAG: RetrievalQA Breaks on 100+ Docs (2026)
Single flat vector store fails at scale — wrong doc surfaces, versions clash, comparisons hallucinate. Fix it with routing, namespaces, RRF, and parent-child retrieval. Full LCEL code.
OpenAI API: Chat, Embeddings & Streaming Without Errors (2026)
OpenAI SDK throwing errors after v1.0? Chat completions, embeddings, and streaming with the current API — copy-paste code that actually runs today.
Text Chunking for RAG: Stop Losing Context in Splits (2026)
Bad chunks ruin good retrieval. Compare fixed, semantic, and hierarchical chunking — with LangChain splitter benchmarks and chunk size test code.
Prompt Engineering: Production Results, Not Vague Output (2026)
Still getting generic LLM responses? Fix prompt structure, system messages, and temperature.
RAG vs Fine-Tuning: Pick Wrong and Waste Weeks (2026)
Building an AI app? Choose wrong between RAG and fine-tuning and burn weeks of effort. Full decision framework, cost comparison, LCEL code, and when to combine both.
Advanced Prompting: Techniques That Beat Basic Patterns (2026)
Basic prompting hit its ceiling? Meta-prompting, self-critique loops, prompt chaining, and agent instruction design — with Python code that works.
AI Agent Evaluation: Catch Failures Before Production (2026)
Agent passing tests but failing users? Trajectory evaluation, tool-use scoring, and goal completion metrics — with LangSmith and Inspect AI code.
Agent Frameworks: LangGraph vs AutoGen vs CrewAI Tested (2026)
Picked the wrong agent framework? Compare LangGraph, AutoGen, and CrewAI on architecture, multi-agent support, and production readiness — with code.
AI Agent Memory: Build Agents That Do not Forget Context (2026)
Agent losing context mid-conversation? Implement short-term buffers, long-term vector memory, and episodic recall.
AI Agent Planning: ReAct & Task Decomposition That Work (2026)
Agents that act before thinking get stuck. ReAct loops, MRKL-style routing, and goal decomposition — implemented in Python with real task examples.
AI Agent Tools: Give LLMs Real-World Capabilities (2026)
An agent without tools is just a chatbot. Build search, code execution, API, and database tools in LangChain.