This next meetup can take our community from conceptual understanding to hands-on enterprise-level implementation of RAGs. Learning Flow:
### 1. Recap and Set the Stage
- Brief recap of the core building blocks of RAG (previous session on 2nd Aug)
- Explain why building an enterprise-grade RAG is different from a PoC (data quality, latency, scale, security, evaluation, etc.).
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### 2. Real-World Case Example & Challenges at Each Stage
Pick a realistic enterprise use case (e.g., HR policy assistant, customer support knowledge base, financial document Q&A).
Flow: (Show actual code outputs at each stage)
- Data Ingestion:
- Challenges: unstructured data, tables, images in PDFs, multilingual text, etc.
- Tools: PDF parsers, OCR, data cleansing pipelines.
- Chunking:
- Challenges: overlapping context, optimal chunk size.
- Demo chunking logic and show how different settings impact downstream quality.
- Embedding & Vector Stores:
- Challenges: embedding quality, model selection, indexing strategies, cost & scalability.
- Show vector outputs and discuss semantic drift issues.
- Retrieval:
- Challenges: precision vs recall, false positives, latency at scale.
- Demo top-k retrieval and show how quality changes with k-values.
- Generation (LLM):
- Challenges: hallucination, instruction-following, answer sourcing.
- Show difference between RAG-constrained output vs raw LLM output.
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### 3. Overcoming Limitations of RAG
- Hybrid search: semantic + keyword for rare terms.
- Metadata filtering: how to control search space for context (e.g., department-specific queries).
- Guardrails: security and content filtering (e.g., access control).
- Evaluation: using metrics like Recall@K, user feedback loops (thumbs up/down).
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### 4. Fine-Tuning RAGs
- Prompt engineering vs fine-tuning: when to choose which.
- Fine-tuning embedding models for domain-specific terminology.
- Fine-tuning LLMs to reduce hallucination.
- In-context learning (few-shot) vs adapter-based methods (LoRA, PEFT).
- Quick demo or code snippet for embedding fine-tuning using a small domain dataset.
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### 5. Multi-Modal RAG:
- Difference between text-only vs multimodal RAG.
- Use cases: technical manuals (images+text), legal contracts (scanned images+tables), customer support (voice+chat+screenshots).
- Building blocks:
- Image/audio/video embeddings
- Unified vector stores
- Multi-modal retrieval.
- Show 1-2 examples of image+text embedding retrieval (even if not full code).
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### 6. End-to-End Demo
- Live build of OneRAG app (FastAPI + LlamaIndex + OpenAI/Cohere embeddings + Chroma/Faiss vector store).
- Show intermediate outputs:
- Original files → cleaned chunks → embeddings (vectors) → vector store → retrieved context → final LLM output.
- Include basic UI (e.g., Streamlit or simple web UI) so participants can ask queries.
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### 7. Q&A and Wrap-up
- Share code and sample datasets.
- Discuss next steps: advanced tuning, multi-modal deep dive, or agentic RAG.???
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## Outcomes:
- Participants will understand the practical challenges and engineering decisions behind RAGs.
- They will see a working enterprise-grade RAG app.
- They will leave with a roadmap for multi-modal RAGs and fine-tuning.
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## Deliverables they can see out of our meetup:
- Full code repo (cleaned & documented).
- Sample dataset (HR policies, financial PDFs, or technical manuals).
- Flow diagram with challenges at each stage
- Recording of the demo - automatic
- Links to fine-tuning & multimodal references / ppt/demo code
