RAG Implementation Guide
From Text to Multimodal
By Madina Gbotoe | Women Defining AI | January 2026
A comprehensive guide for product managers, developers, and AI practitioners building retrieval-augmented generation systems. Covers fundamentals through production deployment, with clear guidance on when to use basic vs advanced techniques.
Download Full Guide (.docx)Sections
10 Parts
Audience
PMs & Developers
Coverage
Basic → Production
Contents
Want the full guide offline?
Download .docxPart 1: RAG Pipeline Overview
RAG systems range from dead simple to highly complex. Understanding the progression helps you build the right level for your use case.
True Minimum RAG (4 Components)
The absolute simplest RAG system needs only four things:
- 1.Document Loading: Get text from your files. Can be as simple as reading .txt files.
- 2.Chunking: Split text into pieces. Can be as simple as splitting by paragraph.
- 3.Search: Find relevant chunks. Can be keyword/string matching - no vectors required.
- 4.Generation: Pass retrieved chunks + query to an LLM. Get answer.
Key insight: No embeddings, no vector database, no fancy infrastructure. Keyword search over text files + LLM = RAG. Good for prototypes, tiny corpora (<50 docs), and learning how RAG works.
Standard RAG (Adds Semantic Search)
Adds embedding model and vector storage to enable semantic (meaning-based) search. Similar meanings = similar vectors.
Advanced RAG (Quality Improvements)
- Hybrid search: Combine keyword (BM25) + semantic search
- Re-ranking: Retrieve more candidates, re-score with second model
- Query expansion: Rewrite or expand queries before searching
- Parent-child retrieval: Search small chunks, return larger context
Choosing Your Level
| Use Case | Recommended Level |
|---|---|
| Prototype / learning | True Minimum (keyword search is fine) |
| Small corpus (<1K docs), exact terminology | Keyword search may be enough |
| Medium corpus, varied queries | Standard RAG with semantic search |
| Large corpus, high accuracy needed | Add hybrid search + re-ranking |
| Enterprise / production | All above + caching, guardrails, fallbacks |
Part 2: Document Preparation Fundamentals
Use Clear Headings and Structure
Organize each guide with descriptive headings, subheadings, and ordered lists. Well-structured documents chunk better.
Consistent Terminology
Use consistent names across documents. If some guides call a part "motor" and others "engine", standardize or note synonyms.
Describe Visuals in Text
For each image or diagram, ensure there is text describing it. This aids retrieval since the system can find images by description.
Maintain Metadata
Organize files with naming conventions that encode metadata. This can be ingested and used to filter or boost relevance.
Part 3: Chunking Strategy
Chunking is arguably the most impactful decision in RAG pipeline design. Poor chunking leads to fragmented context, lost relationships, and degraded answer quality.
Chunk Size Selection
Small (100-300 tokens)
Higher precision, may lose context. Good for FAQ-style.
Medium (300-800 tokens)
Balanced tradeoff. Recommended starting point.
Large (800-1500 tokens)
More context, less precision. For narrative content.
Recommendation: Use semantic chunking with a maximum size fallback. Split first on headers/sections, then on paragraphs, with fixed-size as the last resort. Always use 10-20% overlap.
Part 4: Multimodal Content Processing
Optional UpgradeSkip this section if building text-only RAG. True multimodal RAG requires processing images, tables, and diagrams natively.
Image Processing Strategies
- Vision-Language Models: Use GPT-4V, Claude, or LLaVA to generate detailed descriptions
- CLIP-style embeddings: Embed images directly into the same vector space as text
- OCR for text-heavy images: Extract text from diagrams, flowcharts, screenshots
Table Processing
Tables encode relational information. Flattening to bullet points destroys row-column relationships. Use markdown tables, row-wise statements, or keep tables as atomic units.
Part 5: Embedding and Retrieval Architecture
Embedding Model Selection
- General-purpose: OpenAI text-embedding-3, Cohere embed-v3, BGE, E5, GTE
- Size variants matter: text-embedding-3-small (1536 dims) vs large (3072 dims)
- Dimension tradeoffs: 768-1536 is often the sweet spot
Hybrid Search
Combine semantic search with keyword search (BM25/TF-IDF) for best results. Use reciprocal rank fusion (RRF) to merge results.
Re-ranking
Initial retrieval (top 20-50 chunks) is fast but imprecise. Apply a cross-encoder re-ranker to re-score and select the final top-k. This significantly improves relevance.
Part 6: Prompt Engineering for RAG
Context Presentation
- Use clear delimiters (XML tags or markdown blocks)
- Include source metadata for proper citations
- Place most relevant chunks first
Instruction Framing
- Instruct model to answer based only on provided context
- Request citations to specific sources
- Specify output format expectations
Handling Conflicting Information
When chunks contain contradictory info, instruct the model to surface the conflict, prefer newer sources, or ask for clarification.
Part 7: Quality Metrics and Evaluation
Retrieval Metrics
- Recall@K: % of relevant chunks in top K
- Precision@K: % of returned chunks that are relevant
- MRR: How high does first relevant result appear?
End-to-End Metrics
- Answer correctness: Match to ground truth
- Faithfulness: Grounded in context (no hallucination)
- Answer relevance: Actually addresses the question
Part 8: Edge Cases and Special Considerations
Versioned Documents
Decide: keep only latest, keep all with version metadata, or deduplicate at retrieval time.
Time-Sensitive Info
Content with expiration dates should have date metadata. Consider time-weighted retrieval.
Multilingual Content
Use multilingual embedding models (multilingual-e5, mGTE) for cross-lingual retrieval.
Access Control
Store access control metadata and filter at retrieval time to prevent unauthorized disclosure.
Part 9: Local Deployment Options
When Local Deployment Makes Sense
- Data privacy: Sensitive documents that cannot leave your network
- Cost optimization: High query volumes where API costs are prohibitive
- Offline/air-gapped: No internet access
- Latency control: Eliminating network round-trips
All-in-One Tools
GPT4All (LocalDocs): Point at folder, handles everything. Simple GUI.
LM Studio: Polished GUI for local models. RAG via document upload.
AnythingLLM: Open-source RAG platform with more configuration options.
Part 10: Production Hardening
Most RAG tutorials stop at "it works." Production systems need caching, security, fallbacks, and cost controls.
Caching Strategies
- Embedding cache
- Semantic response cache
- Retrieval cache with TTL
Guardrails & Security
- Rate limiting
- Prompt injection detection
- PII detection and redaction
- Output validation
LLM Fallback Chain
- Primary → Fallback → Static
- Circuit breaker pattern
- Graceful degradation
Cost Controls
- Track token usage
- Per-user cost caps
- Model tiering
Ready to Build Your RAG System?
Download the complete guide for offline reference, or explore the interactive RAG Playground to see these concepts in action.