Building Enterprise AI

Why Your Enterprise Needs Its Own LLM — Fine-Tuning for $15

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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

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    Building Enterprise AIBy Agentic Coders