How To Build Your Own Internal ChatGPT
summary
Building a proprietary LLM is rarely practical unless pursuing world domination in oil and gas, defense, finance, or as a major tech giant; training scale is huge — OpenAI uses 45 terabytes (292M documents) — and retraining runs into ~$700,000 with uncertain outcomes. A pragmatic path uses the GPT-4 API plus prompt architecture: attach persona and company-doc context, run response accuracy checks, and keep logs. An enterprise platform that ingests docs and exposes controlled query channels is live with a few clients.
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chapters (7)
- 0:00Introduction
- 0:56Challenges of Understanding ChatGPT
- 4:46Fine-tuning Challenges
- 9:35Data Size and Cost of Retraining
- 17:37Considerations for Cloud Usage
- 25:55Benefits of Prompt Architecture
- 27:02Springbok's Enterprise Platform
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