Full-stack engineer · Open for freelance work

AI Engineer Lab

AI Engineer Lab is a documentation site and engineering blog for software engineers who ship production systems with AI.

Prompt engineering patterns, model selection, agent workflows, and applied tools. Written as reference documentation, dated like an engineering blog.

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ModelsToolsWorkflowsAgentsAutomationArchitectureField NotesPrompts
Latest writing

Recent guides

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AI Agents Explained: What They Are and How to Start in 2026

AI Agents Explained: What They Are and How to Start in 2026

AI agents explained for engineers: what makes an agent different from a chatbot, the perceive-reason-act loop, common patterns, and how to build your first one.

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Prompt Documentation Template: What to Track

Prompt Documentation Template: What to Track

Prompt documentation template: name, version, output format, test cases, and change log. Copy the Markdown file.

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Prompt Engineering Examples: 12 Copy-Paste Templates

Prompt Engineering Examples: 12 Copy-Paste Templates

Prompt engineering examples for code review, incidents, JSON, and system prompts. Copy the template, swap your input, ship.

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Meta Prompt vs System Prompt (With Examples)

Meta Prompt vs System Prompt (With Examples)

Meta prompt vs system prompt: a meta prompt sets how to think; a system prompt sets identity. Copy-paste examples.

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Best Vibe Coding Platform: Bolt.new vs Lovable vs Replit

Best Vibe Coding Platform: Bolt.new vs Lovable vs Replit

Best vibe coding platform depends on the job. Bolt.new for prompt-to-URL, Lovable for visual React, Replit for teams, Cursor if you already have a repo.

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Download Old and New YouTube Thumbnails — HD YouTube Thumbnail Downloader

Download Old and New YouTube Thumbnails — HD YouTube Thumbnail Downloader

Download old and new YouTube thumbnails in HD. Paste any video URL to grab current thumbnails, browse old and new thumbnail with complete history, and view full size. Free.

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What we publish

AI engineering documentation

Implementation-grade guides and an engineering blog for applied AI engineering — models, prompt engineering, agents, tools, and production workflows.

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Tools

Applied AI engineer tools

Production patterns with Claude Code, Lovable, Supabase, and MERN — workflows that hold up once real engineers use them.

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Models

Picking the right model

Which model to use, when, and why — and how to read benchmarks without getting misled by them.

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Prompts

Prompt engineering lab

Prompt patterns for coding agents and production LLM features — techniques that survive model updates.

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Field notes

Engineering blog and field notes

Dated guides and experiment logs from building with AI — implementation notes, not industry news.

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Available for freelance work

Need a full-stack engineer for your next build?

I'm Alok Kumar, the developer behind AI Engineer Lab. I build production web applications with React, Next.js, Node.js, and Supabase, and I use AI-assisted tools like Claude Code to ship faster without cutting corners.

If you need a hand shipping a product, integrating AI into an existing workflow, or writing technical documentation your engineers will actually use, I'd like to hear about it.

Email me about your projectSee my background

Full-stack web apps

React, Next.js, Node.js, Express, MongoDB, and Supabase — auth, storage, and real-time features included.

AI-assisted development

Integrating Claude Code and LLM-powered workflows into your team's day-to-day engineering process.

Technical documentation

Clear, implementation-first guides and internal docs that hold up once real engineers start using them.

Consulting & code review

Architecture reviews, debugging sessions, and advice on adopting AI tools without breaking your workflow.

Frequently asked questions

What is AI Engineer Lab?
AI Engineer Lab — also searched as AI Engineering Lab — is a documentation site and engineering blog for software engineers who build production systems with AI. It covers model selection, prompt engineering, agent workflows, automation, and applied tools, written for engineers who already know how to code.
Is this AI engineering documentation or a blog?
Both. Guides are structured and categorized like reference documentation, but written with the specificity of an engineering blog — dated, opinionated where it matters, and revised as tools change. There is no separate blog section; articles and field notes live in the same structure as the reference docs.
Does AI Engineer Lab cover prompt engineering?
Yes. The Prompts & Templates section documents prompt engineering patterns for coding agents and production LLM features — techniques that hold up across model updates, not one-off tricks tied to a single model version.
What applied AI engineer tools do you write about?
Claude Code, Lovable, Supabase, MERN stack tooling, vector databases, and LLM APIs from Anthropic and OpenAI. Coverage focuses on integration patterns, workflow design, and what breaks in production — not product announcements or tool roundups.
What is an AI Engineer?
An AI Engineer is a developer who designs and builds systems that use artificial intelligence — typically large language models, vision models, or ML pipelines — as a core component. The role sits at the intersection of software engineering and applied machine learning, with a stronger focus on shipping than on research.
What is the difference between an AI Engineer and an ML Engineer?
An ML Engineer focuses on the machine learning lifecycle — data pipelines, model training, evaluation, and deployment of custom models. An AI Engineer builds applications on top of existing foundation models: LLM API integration, RAG pipelines, agent workflows, prompt management, and production observability.
How do I become an AI Engineer in 2026?
Start with strong software engineering fundamentals — Python, APIs, databases, and version control. Add LLM API integration, vector databases, RAG architecture, prompt engineering, and basic evaluation tooling. What matters most is shipping production systems that use AI reliably, not collecting certificates.
What skills does an AI Engineer need?
Python and at least one backend framework, LLM API integration (Anthropic, OpenAI, or Gemini), vector database fundamentals, RAG patterns, prompt engineering and evaluation, and observability for production AI features. Debugging and monitoring matter more than most courses acknowledge.
What is RAG in AI engineering?
Retrieval-augmented generation (RAG) connects a language model to your own data through embeddings and a vector store. AI engineers use RAG when answers must cite internal documents, stay current, or reduce hallucinations on domain-specific questions. See the Architecture section for implementation patterns.
Does AI Engineer Lab cover AI agents and workflow automation?
Yes. The Agents and Automation sections document agent design patterns, multi-step workflows, and pipeline automation — with code examples and notes on what breaks at scale. Field notes capture experiment results from real builds.
Who is AI Engineer Lab for?
Working software engineers integrating AI into existing products — solo builders, small teams, and developers who became the default AI person on their team. It is not a beginner tutorial site; guides assume you can read code, ship APIs, and debug production issues.
Is AI Engineer Lab free to use?
Yes. All documentation and engineering blog posts are free to read. No account is required. Subscribe to the newsletter if you want an email when new guides are published.