AI Dev Notes: Feb 2, 2026

Explore AI, LLM, RAG, Agent, MCP Techniques with the Twig dev team

Author

Chandan Maruthi
February 02, 2026 • Estimated Reading Time: 2 minutes

Highlights

21 RAG Strategies - over 400 Downloads

I am excited to share the 21 RAG Strategies Ebook was downloaded over 300 times over this weekend. It great to see how important RAG is in todays enterprise AI. Remember to share what you liked or disliked about it - Chandan

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“The future of AI is not about replacing humans, it’s about augmenting human capabilities.”

— Sundar Pichai, CEO of Alphabet/Google.
Engineering Notes

The RAG Journey

Phase 1: The hackathon win

It’s 2025. Your team spins up a RAG demo over a weekend. New models, new tools, vibes are good.

It works. People are impressed. Someone says, “Can we ship this?”
You put it behind a basic UI, wire it to one data source, and move it toward prod. Two weeks. Easy.

Step 2: Reality shows up


A few weeks into real usage, the requests start:

  • “Can we add another data source?”

  • “This team needs Slack, that team needs an internal UI.”

  • “Can it handle PDFs and video?”

  • “Why did it answer this yesterday but not today?”

  • “This answer exists — I know it does.”

  • “Team B shouldn’t see Team A’s data.”

None of these feel crazy. All of them are reasonable.

Step 3: The dev cycles quietly explode

You write them down.
You start scoping.
And you realize this is no longer a 2-week iteration.

Ingestion pipelines. Re-chunking. Re-embedding. Permissions. Retrieval drift. Eval gaps.
What you thought was a quick follow-up turns into a 6–12 month roadmap you never signed up for.

Step 4: What “production RAG” actually means

At this point, it’s obvious: the demo wasn’t the hard part.

Production AI needs layers:

  • Reliable ingestion + re-ingestion

  • Versioned embeddings and retrieval

  • Eval loops and regression checks

  • Permissions and isolation

  • Multiple interfaces, same brain

  • Observability when things go wrong (because they will)

This year, a lot of agents are going to hit production.
And almost none of them will look like the hackathon demo they started from.

That gap — between “it works” and “it holds up” — is where most of the real engineering lives.

About Twig

Learn how you can ship RAG projects faster with Twig

Twig is an AI engineering platform built for teams shipping Retrieval-Augmented Generation (RAG) and agentic systems to production. It automates the hardest parts of enterprise AI—data ingestion, chunking, vectorization, and evaluation—so developers can focus on building intelligent copilots and support automation. Trusted by fast-growing startups and enterprise teams alike, Twig helps you go from prototype to production up to 80% faster.

  • MCP: Use AI inside of your custom apps with Twigs MCP interface

  • Auto-ingestion: Instantly connects and cleans data from sources.

  • Smart chunking: Optimizes context size for precise retrieval.

  • Auto-indexing: Embeds and stores data at scale.

  • Self-evaluation: Monitors and improves answer quality.

  • Fast deploy: Ships RAG pipelines to production 80% faster.