AI Engineering7 min read
RAG evaluation: proving your system actually works
Learn how to evaluate RAG systems quantitatively using RAGAs framework. Measure context relevance, faithfulness, answer correctness, and prove optimizat...
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136 posts in total
AI Engineering7 min read
Advanced RAG with tools: using LlamaIndex
Learn how to use LlamaIndex framework to build advanced RAG systems with tools, sub-question query engines, and ReAct agents. Build production-ready sys...
AI Engineering7 min read
Agentic RAG: building a system that thinks, acts, and corrects
Learn how to build agentic RAG systems with tools, graphs, and self-correction. Move beyond linear chains to dynamic agents that can route, grade, and c...
AI Engineering8 min read
Advanced RAG: building self-correcting systems
Learn how to build agentic RAG systems with routing, grading, and self-correction capabilities. Move beyond linear chains to dynamic graphs that can han...
AI Engineering8 min read
Vector databases and embeddings: the brain of RAG
Learn how embeddings and vector databases power RAG systems. Understand semantic search, cosine similarity, metadata filtering, and choose between open-...
AI Engineering7 min read
Splitting techniques for RAG: the art of the right chunk
Master document chunking for RAG systems. Learn fixed-size, recursive, semantic, and content-aware splitting techniques to improve retrieval quality and...
AI Engineering9 min read
Retrieval-augmented generation (RAG): giving LLMs an open book
How retrieval works in production. Learn how RAG solves LLM limitations by connecting models to external documents. Master chunking, embeddings, vector dat
AI Engineering8 min read
Prompt engineering: how to talk to an LLM
Master prompt engineering techniques for LLMs. Learn specificity, few-shot prompting, role assignment, chain-of-thought reasoning, self-critique, and st...
AI Engineering8 min read
LLM basics: how machines think (and don't)
Learn the fundamentals of Large Language Models (LLMs) for engineers. Understand prediction, tokens, context windows, temperature, cost structure, and c...
AI Engineering8 min read
Architecting AI agents with execution loops
Learn how to design production-ready AI agents with multi-step reasoning, execution loops, safety patterns, checkpointing, and observability. Build auto...
AI Engineering7 min read
Observability and guardrails for production AI
Learn how to implement observability and guardrails in production AI systems. Covering logging, metrics, tracing, cost tracking, input/output validation...
AI Engineering7 min read
Concurrency and resilience in AI systems
Learn how to design production AI systems that handle concurrency gracefully. Covering queue architectures, idempotency, retry strategies, graceful degr...
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Built by Param Harrison
Cofounder of AEOsome.com and Chief Mentor at learnwithparam.com with 14+ years building production systems. I've trained 50+ engineers on AI engineering - these programs distill what actually works into structured paths you can follow at your own pace.
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