Designing LLM Applications for Massive Datasets
summary
Claims a production pipeline that handles 200M+ records by treating the LLM as one modular component, not a monolith, using distributed preprocessing for scale, a statistical extractor (Yake + LLM refinement) to generate candidate topics, and small LLM tasks to deduplicate and normalize results to cut cost and hallucination. Also uses prompt tuning, agentic looped checks, simple regex guards against prompt injection, and evaluation via cross-model judgments (LM-as-judge) plus traditional metrics to validate quality and failure modes.
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chapters (8)
- 0:00LLM apps for massive datasets
- 3:55Pitfalls in LLM design
- 10:26Modular system design
- 15:56Scaling LLM solutions
- 18:47Prompt engineering techniques
- 22:00Evaluating LLM outputs
- 25:11Key takeaways for robust LLM dev
- 26:24Q&A on rules and unstructured data
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