Alex and Maya break down Abhijeet Patil's hands-on account of building a domain-specific LLM for the enterprise — not the theory, not the hype, but the real, things-broke-and-I-fixed-them story.
In this episode:
The agentic AI storm — 88% of organizations are using AI, but nearly two-thirds haven't scaled it (McKinsey 2025)
The Pluribus Syndrome — why foundation models are like a hive mind that sacrifices depth for breadth
The CIO's dilemma — data privacy is the #1 barrier to enterprise AI adoption (IBM, 57% of organizations)
RAG vs. fine-tuning — reference books vs. muscle memory
Why specialists win — DeepSeek's distilled 7B model scored 92.8% on MATH-500 where GPT-4o managed 74.6%
The overqualification trap — over-qualified personnel and overfitting are probably the same term
The experiment — four phases, from everything broken to 95% accuracy
The $15 moment — QLoRA fine-tuning on a single GPU with ~1,000 curated examples
The 77% gut punch — why data quality dominates data quantity, every time
This is Part 1 of a multi-part series. Part 2 will reveal the specific industry use case.
Read the full article: agenticcoders.dev