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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