AI Engineering & LLM Development (page 3 of 7)
Building LLM apps, agents, RAG, MCP, evals, and production AI systems — guides for engineers shipping real AI products.
- Fine Tuning LLMs Explained
Fine-tuning can make an LLM cheaper, faster, and more consistent, but only when you use it for the right problems. This guide explains supervised fine-tuning, preference tuning, reinforcement fine-tuning, evaluation strategy, dataset design, and production rollout patterns.
- Function Calling Explained For LLM Apps
Function calling gives LLM apps a safe, structured way to connect models to real tools, APIs, and business logic. This guide explains how it works, where teams go wrong, and how to implement it in production.
- Gemini vs OpenAI For Production AI Apps
Choosing between Gemini and OpenAI is rarely about branding. It is about the type of product you are building, the tools you need, the latency and reliability profile you can tolerate, and how much platform opinionation you want in production.
- How To Build A Document Chat App With RAG
Learn how to build a document chat application with retrieval-augmented generation, from file upload and chunking to retrieval pipelines, answer generation, citations, and production reliability.
- How To Build A RAG App Step By Step
A practical step-by-step guide to building a RAG app that actually works in production, including ingestion, chunking, embeddings, retrieval, prompting, evaluation, and reliability patterns.
- How To Build An AI Agent With Tool Use
A practical guide to building AI agents with tool use, from tool definitions and orchestration loops to approvals, error handling, memory, and production hardening.
- How To Build An Eval Driven AI Workflow
A practical guide to building eval-driven AI workflows with test sets, graders, offline and online evaluation, tracing, release gates, and continuous improvement loops.
- How To Build An LLM App From Scratch
A practical step-by-step guide to building an LLM app from scratch, from choosing the right problem and model to shipping, evaluating, and improving a production-ready system.
- How To Catch Hallucinations Before Production
A practical guide to finding and reducing hallucinations in LLM apps before launch using eval-driven development, guardrails, grounding, fact-checking, and production-safe rollout.
- How To Choose The Right AI Stack For Your App
A practical guide to choosing the right AI stack for your app, with clear recommendations for simple LLM apps, RAG systems, agentic workflows, and production-ready AI backends.