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AI engineeringField notesBuilds

The AI Build RoomWhere AI ideas become real software.

Practical AI engineering — how AI systems are designed, built, debugged and shipped. Guides, architecture, experiments and production lessons.

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Jump toLLMsRAG

User → App → Model → Tools / Data → Result

System / One AI requestFig. 01
  1. Userasks a question
  2. Applicationcontext · auth · cache
  3. Modeltokens in, tokens out
    • Tools
    • Data
  4. Resultstreamed back

Every build in this room is a variation of this path.

01Explore the room

The index. Pick a system.

Eight areas of practical AI engineering. Each one filters the builds below.

  1. LLMs02 buildsHow large language models behave: tokens, context windows, sampling and their failure modes.
  2. RAGIn the workshopRetrieval-augmented generation from chunking and embeddings to retrieval quality and grounding.
  3. AI Agents03 buildsTool calling, planning loops, memory and the guardrails that keep agents on task.
  4. Generative AIIn the workshopPractical generative AI patterns beyond the demo: structured output, multimodal and evaluation.
  5. AI Engineering02 buildsThe day-to-day engineering of AI features: testing, debugging, evaluation and iteration.
  6. AI Architecture01 build

02Latest builds

Fresh from the bench.

Experiments, engineering notes and production lessons. Filter by topic, or search above.

03Build by problem

I want to…

Start from what you're building. Each problem opens the builds that solve it, in reading order.

  1. Build a Chatbot02 related builds →
  2. Build a RAG System01 related build →
  3. Build an AI Agent03 related builds →
  4. Deploy AI on AWS02 related builds →
  5. Reduce AI Costs02 related builds →
  6. Debug an AI Application

Build something.Understand why.

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Builds in LLMs2 builds

  • LLMsBuild

    Understanding Context Windows: How LLMs Actually Use Information

    A context window is the model's whole workspace for one request. Here's what fills it, why models miss what's buried inside, and how to manage it in real applications.

    • Context Window
    • LLM
    • RAG
    • Prompt Engineering
    • Cost Optimization
    Faizan Syed·7 min·06 Aug 2026·98 viewsRead →
  • LLMsBuild

    Why LLM Responses Become Inconsistent

    Same prompt, different answers. Sampling, prompt drift, model updates and hidden context changes all contribute. Here is how to make output predictable enough to ship.

    • LLM
    • Prompt Engineering
    • Evaluation
    The AI Build Room·2 min·02 Jul 2026·52 viewsRead →
System design for AI applications: pipelines, boundaries, data flow and tradeoffs.
  • AWS01 buildRunning AI workloads on AWS: Lambda, containers, Bedrock and the operational details.
  • Node.jsIn the workshopBuilding AI backends in Node.js: streaming, concurrency, retries and resilience.
  • TypeScriptIn the workshopType-safe AI integrations: typed tool schemas, validated model output and safer refactors.
  • AI Security01 buildPrompt injection, data exfiltration, tool permissions and securing LLM-powered systems.
  • AI Performance01 buildLatency, throughput, caching and cost: making AI features fast and affordable.
  • Production AI01 buildWhat changes after launch: observability, cost control, incidents and reliability.
  • AI + Development01 buildBuilding AI features into real applications: APIs, streaming and the code around the model.
  • 03 related builds →
  • Understand LLMs02 related builds →