Introduction
The AI landscape in 2026 is wild. A year ago I was still copying prompts from Twitter threads. Now I am building RAG pipelines and shipping real products powered by large language models. Here is what I have learned along the way.
Getting Started Was Messy
My first attempt at building something with an LLM was rough. I tried to build a chatbot that could answer questions about my notes. It hallucinated constantly, lost context after 3 messages, and occasionally outputted nonsense.
The breakthrough came when I stopped trying to make the model do everything and started treating it as one piece of a larger system.
What Actually Worked
1. RAG Over Fine-tuning
For most use cases, retrieval-augmented generation beats fine-tuning. It is cheaper, faster to iterate on, and you do not need to retrain when your data changes.
2. Structured Output is a Game Changer
Getting the model to return JSON instead of free-text changed everything. Suddenly I could pipe LLM output directly into my application logic without fragile regex parsing.
3. Prompt Engineering is Real Engineering
Good prompts are not magic words. They are carefully structured instructions with clear constraints, examples, and edge case handling. I now version-control my prompts like I version-control my code.
4. Open-Source Models Are Catching Up
I have been running Mistral, Llama, and Qwen locally. For many tasks, they are indistinguishable from the big proprietary APIs. And you get privacy, no rate limits, and zero cost per token.
My Current Stack
- Python for everything (with asyncio for concurrent API calls)
- LangChain for orchestration
- ChromaDB for vector storage
- Ollama for running local models
- Streamlit for quick demos
Mistakes I Made (So You Do Not Have To)
- Over-engineering early: My first RAG pipeline had 15 components. I rebuilt it with 4 and it worked better.
- Ignoring evaluation: You cannot improve what you cannot measure. Build evals from day one.
- Chasing every new model: I wasted weeks switching between models. Pick one, learn its quirks, ship something.
- Not reading the docs: The model provider docs usually answer your question.
What Is Next
I am exploring multi-agent architectures, fine-tuning with LoRA on domain-specific data, and building evals that actually correlate with user satisfaction.
Always happy to chat about AI, Python, or whatever you are building. Drop a comment or find me on GitHub.
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