Building & Scaling Enterprise RAG Apps – From Code to Multi-Modal RAGs

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)

1. Data Ingestion:

• Challenges: unstructured data, tables, images in PDFs, multilingual text, etc.
• Tools: PDF parsers, OCR, data cleansing pipelines.

2. Chunking:

• Challenges: overlapping context, optimal chunk size.
• Demo chunking logic and show how different settings impact downstream quality.

3. Embedding & Vector Stores:

• Challenges: embedding quality, model selection, indexing strategies, cost & scalability.
• Show vector outputs and discuss semantic drift issues.

4. Retrieval:

• Challenges: precision vs recall, false positives, latency at scale.
• Demo top-k retrieval and show how quality changes with k-values.

5. 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:

1. Full code repo (cleaned & documented).
2. Sample dataset (HR policies, financial PDFs, or technical manuals).
3. Flow diagram with challenges at each stage
4. Recording of the demo - automatic
5. Links to fine-tuning & multimodal references / ppt/demo code