Showing results for "rag"
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RAG Hallucination Control & Enterprise SearchAI Bootcamp- Explore use of RAG in enterprise settings with citation engines - Compare hallucination reduction strategies: constrained decoding, retrieval, DPO - Evaluate model trustworthiness for sensitive applications - Learn from production examples in legal, compliance, and finance contexts
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Advanced RAG & Retrieval MethodsAI Bootcamp- Analyze case studies on production-grade RAG systems and tools like Relari and Evidently - Understand common RAG bottlenecks and solutions: chunking, reranking, retriever+generator coordination - Compare embedding models (small vs large) and reranking strategies - Evaluate real-world RAG outputs using recall, MRR, and qualitative techniques - Learn how RAG design changes based on use case (enterprise Q&A, citation engines, document summaries)
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RAG & Retrieval Techniques (Mini Project 2)AI Bootcamp- Understand the full RAG pipeline: pre-retrieval, retrieval, and post-retrieval stages - Learn the difference between term-based and embedding-based retrieval methods (e.g., TF-IDF, BM25 vs. vector search) - Explore vector databases, chunking, and query optimization techniques like HyDE, reranking, and filtering - Use contrastive learning and cosine similarity to map queries and documents into shared vector spaces - Practice retrieval evaluation using `recall@k`, `precision@k`, and `MRR` - Generate synthetic data using LLMs (Instructor, Pydantic) for local eval scenarios - Implement baseline vector search pipelines using LanceDB and OpenAI embeddings (3-small, 3-large) - Apply rerankers and statistically validate results with bootstrapping and t-tests to build intuition around eval reliability
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RAG in Enterprise SettingsAI BootcampExplore RAG for enterprise use (citation engines), compare hallucination reduction strategies (constrained decoding, retrieval, DPO).
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RAG Evaluation and OptimizationAI BootcampEvaluate RAG outputs (recall, MRR, qualitative), optimize retriever+generator coordination for enterprise use cases.
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Advanced RAG SystemsAI BootcampAnalyze production-grade RAG case studies (Relari, Evidently), understand bottlenecks (chunking, reranking), and compare embedding models.
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RAG Evaluation & ImplementationAI BootcampEvaluate RAG with `recall@k`, `precision@k`, `MRR`, generate synthetic data with LLMs, and implement baseline vector search with LanceDB and OpenAI embeddings.
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RAG Pipeline OverviewAI BootcampUnderstand the RAG pipeline (pre-retrieval, retrieval, post-retrieval) and compare term-based vs embedding-based retrieval (TF-IDF, BM25 vs vector search).
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Step 11 - The Storage