Try it Yourself: Fine-Tune an LLM using RAG


Key Considerations when Building RAG

  • Robust Data Pipelines

    -Multiple strategies for ingestion, chunking, embedding generation

    -Many Vector DBs with different tradeoffs/choices.

    -Data Processing needs to be industrialized

  • Richer Context = Better Results

    -Adding Real-time and other structured context to Vectors improves results

  • No One Right Model

    -Not all models are equal. Results, performance, and cost vary a lot

    -Open Source vs Commercial

    -Hosted vs Privately Installed

    -Model size from Billions to Trillion Parameters

    -Pruned Models running at small size/cost

  • Model Output Doesn't Have to be the End

    -Use Model output as a data transform/input to another step

  • Model Hallucinations are Real

    -Solved with better context and result validations

  • Keys to Production-Grade Success

    -Experiment Heavily and Rapidly

    -You might end up using multiple models each for different use case

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Enhancing LLM Operations with Private Data using RAG

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