What is RAG? · How it works · RAG vs Fine-tuning · Prompt engineering for RAG · Build a RAG pipeline · Tools & stack · Evaluation · Use cases · FAQ
Retrieval Augmented Generation (RAG) is an AI architecture that enhances a large language model by retrieving relevant information from an external knowledge base before generating a response. Instead of relying solely on static training data, the model receives real-time, targeted context — producing answers that are more accurate, current, and grounded in your specific documents, data, or business knowledge.
- What it solves: Knowledge cutoffs, hallucinations, and lack of domain-specific accuracy in LLMs
- How it works: Embed documents → store in vector DB → retrieve relevant chunks at query time → inject into prompt → generate
- RAG vs Fine-tuning: RAG for dynamic knowledge; fine-tuning for fixed behavior/tone changes
- Best frameworks: LlamaIndex (RAG-first), LangChain (broader agent workflows)
- Best embedding model: OpenAI text-embedding-3-small (cost-efficient); BGE models (open-source)
- Best vector DB: ChromaDB (dev/small scale), Pinecone (production), Weaviate (open-source production)
- Chunking default: 300–600 tokens with 10–15% overlap — tune from there
- No-code options: Dify, Flowise, Botpress — viable for SMBs without developers
- RAG is not a model — it is a system. You combine an embedding model, a vector database, a retrieval mechanism, and an LLM into a single pipeline.
- RAG does not require retraining. You can update your knowledge base instantly without touching the underlying model.
- Chunking quality determines retrieval quality. Poor chunking is the single most common cause of bad RAG outputs.
- RAG and fine-tuning are complementary, not competing. High-performing production systems often use both.
- Small businesses can implement RAG affordably. A basic RAG system over 100 documents costs roughly $5–$20/month to run at modest volume.
What Is Retrieval Augmented Generation (RAG)?
Most LLMs — GPT-4, Claude, Gemini — are trained on a fixed dataset with a knowledge cutoff. Ask them about your company's internal policy document, last month's customer data, or a contract signed yesterday and they cannot answer accurately. They either hallucinate an answer or admit they do not know.
RAG solves this by giving the model a knowledge retrieval mechanism at query time. The system searches your external documents for relevant passages, injects those passages directly into the prompt as context, and the model generates its answer from those retrieved facts rather than from memory alone.
The term was coined in a 2020 paper by Facebook AI Research (Lewis et al.) and has since become the dominant architecture for building knowledge-intensive AI applications in production.
Think of a RAG system as an open-book exam. Without RAG, the LLM must answer from memory — prone to outdated facts and fabrication. With RAG, the model has the textbook open in front of it during the exam. It still needs intelligence to synthesize and reason, but it no longer needs to memorize everything.
How RAG Works: Step by Step
Document Ingestion & Chunking
Source documents (PDFs, Word files, web pages, databases) are loaded and split into smaller chunks — typically 300–600 tokens each with a small overlap. This makes individual passages retrievable without loading entire documents.
Embedding Generation
Each text chunk is passed through an embedding model (e.g., OpenAI text-embedding-3-small), which converts it into a dense numeric vector — a representation of its semantic meaning in high-dimensional space.
Vector Storage
The vectors and their corresponding text chunks are stored in a vector database (Pinecone, Weaviate, ChromaDB). This database is designed to perform fast similarity searches across millions of vectors.
Query Embedding
At inference time, the user's question is embedded using the same embedding model used during ingestion. This creates a query vector in the same semantic space as your stored document vectors.
Similarity Retrieval
The vector database performs an approximate nearest-neighbor search, returning the top-k most semantically similar chunks (typically 3–8). This is your retrieved context.
Prompt Augmentation & Generation
The retrieved chunks are injected into the LLM's prompt alongside the original user query. The model synthesizes the context and generates a grounded, accurate response — citing specific retrieved passages where instructed.
RAG vs Fine-Tuning: Which Should You Use?
This is the most common strategic question teams face when adding domain knowledge to an LLM. The answer is not either/or — but understanding the trade-offs is essential before committing budget.
Use RAG when your knowledge is dynamic, large, or needs to be updated frequently — no model retraining required. Use fine-tuning when you need to change how the model behaves, responds stylistically, or reasons about a specific format — not what it knows.
| Dimension | RAG | Fine-Tuning |
|---|---|---|
| What it changes | What the model knows at inference time | How the model behaves (weights) |
| Knowledge update | Instant — update the vector DB, no retraining | Requires full retraining cycle (hours to days) |
| Best for | Company docs, FAQs, live data, large knowledge bases | Specific output formats, tone, reasoning style |
| Hallucination risk | Lower — grounded in retrieved text | Can introduce new hallucinations if training data is noisy |
| Cost | Embedding + vector DB + inference (~$5–$50/mo for SMBs) | Training compute: $500–$10,000+ depending on model size |
| Transparency | High — you can inspect retrieved chunks | Low — knowledge is baked into weights (black box) |
| Context window use | Consumes tokens for retrieved context | Knowledge lives in weights — no context tokens spent |
| When NOT to use | When behavior/style needs to change | When knowledge changes frequently |
Fine-tune the model on your company's communication style and output format, then use RAG to inject the current knowledge it needs to answer correctly. This is the architecture used by the most reliable production AI systems in 2026.
RAG vs Embeddings: Understanding the Relationship
A common point of confusion: embeddings are a component of RAG — they are not competing approaches. Embeddings are the mathematical representation that powers semantic search. RAG is the end-to-end system that uses embeddings to retrieve context.
| Concept | What It Is | Role in RAG |
|---|---|---|
| Embeddings | Dense vector representations of text capturing semantic meaning | Used to encode both documents and queries for similarity comparison |
| Vector Database | Specialized database optimized for storing and searching embedding vectors | Stores and retrieves embeddings at query time |
| RAG Pipeline | End-to-end system combining ingestion, embedding, retrieval, and generation | The overall architecture — embeddings and vector DBs are components within it |
Context Window vs RAG: When Does RAG Beat Stuffing Everything Into the Prompt?
GPT-4 now supports 128K token context windows. A reasonable question: why not just paste all your documents into the prompt?
| Factor | Full Context Stuffing | RAG |
|---|---|---|
| Cost | Expensive — you pay for every token in the prompt, every time | Cheaper — only the 3–8 most relevant chunks are injected |
| Latency | Slower — large prompts take longer to process | Faster — small, targeted context |
| Accuracy | Degrades with length — LLMs suffer "lost in the middle" attention issues | Higher signal-to-noise — only relevant content reaches the model |
| Scalability | Breaks above ~500 pages even with 128K window | Scales to millions of documents |
| Best for | Small, static documents (<50 pages); one-off analysis tasks | Production apps with large or frequently updated knowledge bases |
Research consistently shows that LLMs are better at using information at the beginning and end of long contexts than in the middle. For knowledge bases larger than 20–30 pages, RAG with targeted retrieval outperforms full-document context stuffing — even when the context window is technically large enough to fit everything.
RAG for Prompt Engineers: Techniques, Templates & System Prompts
If you are building or optimizing a RAG system, the quality of your prompts is just as important as the quality of your retrieval. Here is the practitioner playbook.
The System Prompt for RAG: A Production-Ready Template
The system prompt defines how the model uses the retrieved context. A weak system prompt leads to the model ignoring context, hallucinating, or blending retrieved facts with prior training knowledge.
The instruction "Do not use any knowledge from your training data" is the most critical line in a RAG system prompt. Without it, the model will seamlessly blend retrieved facts with memorized (potentially outdated or incorrect) knowledge — and you cannot tell the difference in the output.
RAG Prompting Techniques That Improve Output Quality
| Technique | What It Does | When to Use |
|---|---|---|
| Source citation instruction | Tells the model to cite which chunk each fact came from | Any application where factual trust matters (legal, finance, support) |
| Grounding assertion | "Answer ONLY using the provided context" | Always — this is table stakes for production RAG |
| Uncertainty fallback | Explicit "I don't know" instruction when context is insufficient | Customer-facing chatbots; anywhere hallucinations are unacceptable |
| Step-back prompting | Ask a more general query first to retrieve broader context, then the specific query | Complex multi-hop questions that require background knowledge |
| HyDE (Hypothetical Doc Embeddings) | Generate a hypothetical answer first, embed that, use it to retrieve relevant chunks | Improving retrieval for vague or abstract queries |
| Multi-query retrieval | Generate 3–5 query variations, retrieve for all, deduplicate | When single-query retrieval misses relevant chunks |
RAG Prompt Templates for Common Business Use Cases
How to Build a RAG Pipeline: Architecture & Workflow
Building a RAG pipeline breaks down into four phases: data preparation, indexing, retrieval, and generation. The decisions you make in each phase cascade — poor chunking cannot be fixed at the retrieval layer, and poor retrieval cannot be fixed by a better prompt.
Chunking Strategy for RAG: Getting This Right First
Chunking is the most underestimated decision in the entire RAG pipeline. The wrong strategy is the leading cause of poor retrieval — and poor retrieval produces poor answers regardless of how good your embedding model or LLM is.
| Strategy | How It Works | Best For | Typical Size |
|---|---|---|---|
| Fixed-size | Split at fixed token/character count with overlap | General-purpose; good default starting point | 300–600 tokens, 10–15% overlap |
| Sentence-based | Split at sentence boundaries using NLP | Conversational content, Q&A pairs, FAQ documents | 1–5 sentences per chunk |
| Recursive character | Try splitting at paragraphs → sentences → characters progressively | General documents; default in LangChain's splitter | Configurable |
| Semantic chunking | Split where meaning shifts using embedding similarity | Long unstructured documents where topics shift | Variable |
| Hierarchical (parent-child) | Store summary-level and detail-level chunks together | Long reports, contracts, technical manuals | Summary: full doc; child: 200–400 tokens |
| Document-specific | Respect natural structure: headings, tables, code blocks | Markdown docs, code, structured reports | Section-level |
Always include a token overlap (50–100 tokens) between consecutive chunks. Without overlap, answers that span chunk boundaries — a sentence that starts in one chunk and concludes in the next — are permanently severed and become unrecoverable.
Choosing an Embedding Model for RAG
| Model | Provider | Cost | Dimensions | Best For |
|---|---|---|---|---|
| text-embedding-3-small | OpenAI | $0.02/M tokens | 1,536 | Best default; cost-efficient; great retrieval quality |
| text-embedding-3-large | OpenAI | $0.13/M tokens | 3,072 | When retrieval accuracy is critical; production grade |
| embed-english-v3.0 | Cohere | ~$0.10/M tokens | 1,024 | Strong alternative; native re-ranking support |
| BGE-large-en-v1.5 | BAAI (open-source) | Free (self-hosted) | 1,024 | Best open-source model; use with sentence-transformers |
| all-MiniLM-L6-v2 | sentence-transformers | Free (self-hosted) | 384 | Lightweight; local dev; low-resource environments |
| voyage-3 | Voyage AI | ~$0.06/M tokens | 1,024 | Strong domain-specific retrieval (legal, finance) |
Always use the same embedding model to encode your documents AND your queries. Mixing models (even two from the same provider at different versions) produces incompatible vector spaces and destroys retrieval quality. If you upgrade your embedding model, you must re-embed your entire document corpus.
Vector Database for RAG: Choosing the Right One
ChromaDB Open Source
Lightweight, embedded vector database. Runs in-process — no server needed. Ideal for prototyping and small deployments (<100K documents).
Pinecone Managed Cloud
Fully managed, serverless vector database with automatic scaling. No infrastructure management. Free tier available; paid plans from $70/month. Easiest path to production.
Weaviate Open Source / Cloud
Open-source vector database with flexible schema, hybrid search (BM25 + dense vectors), and built-in ML model integrations. Self-host or use Weaviate Cloud.
Qdrant Open Source / Cloud
High-performance Rust-based vector database. Excellent filtering, payload support, and performance at scale. Self-host or use Qdrant Cloud.
pgvector PostgreSQL Extension
Adds vector similarity search directly to PostgreSQL. If you already run Postgres, this avoids adding a new infrastructure component entirely.
FAISS Open Source (Meta)
Facebook's high-performance library for dense vector search. In-memory; requires custom persistence. Powerful but lower-level than managed options.
RAG Tools & Frameworks: LangChain vs LlamaIndex
Two frameworks dominate the RAG ecosystem in 2026. Choosing correctly from the start saves significant refactoring time.
| Factor | LlamaIndex | LangChain |
|---|---|---|
| Primary strength | Data ingestion, indexing, and RAG pipelines — purpose-built | Full agent workflows, tool use, multi-step chains |
| RAG out of the box | Excellent — best abstractions for chunking, indexing, retrieval | Good — more manual configuration required |
| Learning curve | Moderate — RAG concepts map directly to API | Steeper — more concepts to learn upfront |
| Agent support | Available (LlamaIndex Agents) but secondary | Mature — this is LangChain's core strength |
| Ecosystem | Tight integrations with 160+ data sources | Broadest tool/integration ecosystem overall |
| Use RAG as standalone | ✅ First choice | ⚠️ Works but more verbose |
| RAG as part of agent | ⚠️ Possible | ✅ First choice |
RAG with OpenAI Embeddings + LlamaIndex: Minimal Working Example
RAG with LangChain + Pinecone
RAG Evaluation Metrics & Hallucination Reduction
Shipping a RAG system without measuring its quality is the fastest way to erode user trust. The following metrics form the standard evaluation framework for production RAG.
Core RAG Evaluation Metrics
| Metric | What It Measures | Target | Tool to Measure |
|---|---|---|---|
| Faithfulness | Is the answer grounded in retrieved context? Or did the model hallucinate beyond it? | > 90% | RAGAS, TruLens |
| Answer Relevance | Does the generated answer actually address the user's question? | > 85% | RAGAS, LangSmith |
| Context Precision | Of the chunks retrieved, what fraction were actually relevant? | > 80% | RAGAS |
| Context Recall | Were all the relevant chunks in your corpus actually retrieved? | > 75% | RAGAS |
| Latency (p95) | End-to-end query response time at 95th percentile | < 3s for chat apps | LangSmith, custom monitoring |
| Answer Completeness | Did the answer fully address all parts of a multi-part question? | Qualitative | LLM-as-judge evaluation |
RAG Hallucination Reduction: Practical Techniques
- Grounding instruction in system prompt: The single highest-impact change — explicitly instruct the model to answer only from retrieved context (template above).
- Add a "no-information" fallback: Define what the model should say when context is insufficient. Silence on this creates hallucination pressure.
- Hybrid search (dense + sparse): Combine vector similarity search with BM25 keyword search. Captures both semantic matches and exact-term matches, improving retrieval comprehensiveness.
- Re-ranking retrieved chunks: Use a cross-encoder reranker (Cohere Rerank, FlashRank) to re-score the top-20 retrieved chunks and take only the top-5. Improves precision significantly.
- Source citation in output: Require the model to cite source chunks in its response. This creates accountability and makes hallucinations detectible.
- Self-RAG patterns: Have the model evaluate whether it needs to retrieve, retrieve again, or answer from current context — reduces unnecessary context injection and focuses generation.
- Smaller, more focused chunks: Larger chunks often include irrelevant content that dilutes the signal. Tighter chunks with higher precision reduce noise reaching the model.
RAG Use Cases for Small Businesses, Freelancers & Startups
RAG amplifies what skilled human professionals can do — it doesn't replace them. See how virtual staffing and AI integration combine human expertise for better business outcomes.
RAG is not reserved for enterprise AI teams. The economics work at any scale — a basic RAG system over 100 company documents costs roughly $5–$20/month in API and infrastructure costs at typical SMB query volumes. For SMBs looking to further reduce operational overhead, see why startups and SMEs should outsource accounting.
📞 Customer Support Chatbot
Build a chatbot over your product documentation, FAQ, and support history. Customers get instant, accurate answers 24/7 without hiring more agents.
📄 Contract & Document Q&A
Law firms, accountants, and consultants can query contracts, financial reports, and client documents conversationally. For firms needing remote finance and accounting staff, Zedtreeo provides pre-vetted professionals. "What are the termination clauses in Acme's contract?" becomes answerable in seconds.
🧾 Accounting & Bookkeeping Intelligence
Index invoices, bank statements, and transaction records. Need a virtual bookkeeping assistant? Zedtreeo provides pre-vetted remote finance staff. Ask natural-language questions: "Which vendors did we pay over $5K last quarter?" or "What's our outstanding accounts payable as of this week?"
🏪 Internal Knowledge Base Assistant
Onboard new hires faster by making your entire wiki, SOPs, and process documents conversationally queryable. Reduces "who do I ask?" interruptions to senior staff.
🛒 E-commerce Product Assistant
Let shoppers ask natural-language questions about products: "Which laptop has the longest battery life under $1,200?" instead of filtering manually. Index product descriptions, specs, and reviews.
📊 Sales Enablement RAG
Give sales reps instant access to case studies, pricing guides, competitive battlecards, and proposal templates through a chat interface. No more digging through shared drives mid-call.
Dify (dify.ai) and Flowise (flowiseai.com) allow non-technical business owners to build RAG applications over their own documents with drag-and-drop interfaces — no Python required. For simple knowledge-base chatbots under 200 documents, these tools deploy in under a day.
RAG Pros & Cons: An Honest Assessment
✅ Advantages of RAG
- No model retraining — update knowledge instantly
- Dramatically reduces hallucinations with grounding
- Transparent — you can inspect what was retrieved
- Scales to millions of documents
- Works with any LLM (GPT-4, Claude, Llama, Gemini)
- Cost-efficient at modest query volumes
- Enables citations and source attribution
- Easily testable and measurable with RAGAS
❌ Limitations of RAG
- Retrieval quality is only as good as your chunking
- Adds latency (retrieval step + vector search)
- Consumes context window tokens for retrieved content
- Cannot change model behavior or output style
- Multi-hop reasoning (requiring 3+ document lookups) is hard
- Requires ongoing maintenance as documents change
- Noisy or poorly formatted source documents degrade quality
Common Mistakes When Building RAG Systems
Mistake 1: Treating Chunking as an Afterthought
Using the default chunking settings without evaluating retrieval quality leads to chunks that either split mid-sentence or are so large they dilute relevance.
✅ Fix: Evaluate your chunks manually on 20–30 representative queries before going to production. Look at what is actually being retrieved — not just the generated answer.
Mistake 2: Using Cosine Similarity Alone for Retrieval
Dense vector search alone misses exact keyword matches — important for product names, contract clauses, part numbers, and proper nouns.
✅ Fix: Implement hybrid search. Combine dense retrieval (semantic similarity) with sparse retrieval (BM25 keyword matching) using an ensemble retriever. Both Weaviate and LangChain support this natively.
Mistake 3: No Evaluation Framework Before Shipping
Deploying a RAG system without measuring faithfulness and context precision means you have no baseline to improve from — and no way to detect regressions.
✅ Fix: Set up RAGAS or TruLens before launch. Define 50–100 test queries with expected answers. Run evaluations against your test set after every significant change to chunking, retrieval, or prompts.
Mistake 4: Retrieving Too Many Chunks (or Too Few)
Setting k=20 floods the context window with noise. Setting k=1 risks missing complementary context. Both hurt output quality.
✅ Fix: Start with k=5 and measure context precision. If precision is low, add a re-ranker before sending chunks to the LLM. Most production systems land between k=3 and k=8 after optimization.
Mistake 5: Ignoring Document Quality in the Ingestion Phase
Garbage in, garbage out. PDFs with poor OCR, scanned images without text extraction, or unstructured HTML dumps all produce low-quality chunks that degrade retrieval.
✅ Fix: Invest in a proper document parsing layer before embedding. Tools like Unstructured.io, LlamaParse, or Docling handle complex document formats, tables, and multi-column layouts far better than basic text extraction.
FAQ: Retrieval Augmented Generation
What is RAG in AI?
RAG (Retrieval Augmented Generation) is an AI architecture that enhances a large language model by retrieving relevant information from an external knowledge base before generating a response. Instead of relying solely on training data, the LLM receives real-time context — making outputs more accurate, up-to-date, and grounded in your specific data. RAG was introduced by Facebook AI Research in 2020 and has since become the dominant approach for building knowledge-intensive AI applications.
What is the difference between RAG and fine-tuning?
RAG retrieves external information at inference time and injects it into the prompt — no model retraining required. Fine-tuning modifies the model's weights through additional training on domain-specific data, changing how it behaves rather than what it knows. RAG is better for dynamic, frequently updated knowledge bases. Fine-tuning is better for teaching the model a specific tone, reasoning style, or output format. The two are complementary — production systems often use both.
What is the context window vs RAG?
The context window is the maximum text an LLM can process in a single prompt — up to 128K tokens for GPT-4. RAG is a strategy for accessing knowledge beyond what fits in the context window by retrieving only the most relevant chunks at query time. Even when context windows are large enough to hold all your documents, RAG is more cost-efficient (you pay per token) and improves accuracy by filtering irrelevant content before it reaches the model.
Do I need a vector database for RAG?
For most production RAG systems, yes. A vector database stores your document embeddings and enables fast semantic similarity search at query time. ChromaDB runs locally and works well for small datasets and development. Pinecone and Weaviate are better choices for production workloads requiring scale, reliability, and low-latency search. If you already use PostgreSQL, pgvector is a practical option that avoids adding new infrastructure for datasets under ~1 million vectors.
What is the best embedding model for RAG?
OpenAI's text-embedding-3-small offers the best balance of cost ($0.02 per million tokens) and performance for most business use cases. For maximum retrieval accuracy in production, text-embedding-3-large is stronger. For fully open-source and self-hosted deployments, BGE-large-en-v1.5 from BAAI is the top-performing free option. Always use the same model for both document ingestion and query embedding — mixing models breaks retrieval.
Is LangChain or LlamaIndex better for RAG?
LlamaIndex is the better choice for RAG-first applications — it has the most mature and opinionated abstractions for document ingestion, chunking, indexing, and retrieval. LangChain is better when RAG is one component within a broader agent, tool-use, or multi-step workflow system. Both are mature and production-ready. If your primary goal is building a document Q&A or knowledge base assistant, start with LlamaIndex.
How does RAG reduce hallucinations?
RAG reduces hallucinations by anchoring the LLM's response to retrieved, verified source documents. The system prompt explicitly instructs the model to answer only from the provided context — not from training memory. This means the model cannot fabricate facts that are absent from your knowledge base. Adding source citations, a "I don't know" fallback instruction, and faithfulness evaluation (using RAGAS) further reduces hallucinated outputs in production.
Can small businesses use RAG without coding?
Yes. No-code tools like Dify, Flowise, and Botpress allow small businesses to build RAG-powered applications over their own documents without writing any code. For a customer support chatbot, internal knowledge base, or document Q&A system, these platforms deploy in under a day. For more customization, basic Python skills and LlamaIndex or LangChain open up a much wider range of possibilities — or you can hire a remote AI prompt engineer for a 1–2 week implementation.
What chunking strategy is best for RAG?
Fixed-size chunking (300–600 tokens with 50–100 token overlap) is the most reliable default for general-purpose documents. Sentence-based chunking works better for conversational or Q&A content where complete thoughts need to stay together. For long structured documents like contracts or technical reports, hierarchical chunking — storing both a summary chunk and detail chunks — typically yields the best retrieval precision. Always evaluate chunk quality on a sample of real queries before production.
Practical Recommendations: Which RAG Stack for Your Situation?
🗺️ RAG Stack Selector by Use Case & Team Size
| Situation | Recommended Stack | Why |
|---|---|---|
| Solo freelancer / no code | Dify or Flowise + ChromaDB | Deploy in <1 day; no infrastructure management |
| Small business, basic chatbot | LlamaIndex + ChromaDB + GPT-4o | Simple setup; runs locally or on a small VPS |
| Startup going to production | LlamaIndex + Pinecone + OpenAI embeddings + GPT-4o | Managed vector DB; scales automatically; fast to launch |
| Need open-source everything | LangChain + Weaviate + BGE embeddings + LLaMA 3 | No vendor lock-in; self-hostable; GDPR-friendly |
| Already on PostgreSQL | LlamaIndex + pgvector + OpenAI embeddings | No new infra; leverage existing DB expertise |
| Complex agents + RAG | LangChain + Qdrant + Cohere Rerank | LangChain's agent ecosystem + Qdrant's filtering + reranking for precision |
| Accounting / legal doc Q&A | LlamaIndex + Pinecone + Voyage AI embeddings + LlamaParse | Voyage excels at legal/finance; LlamaParse handles complex PDFs |
Ready to Build a RAG System for Your Business?
Whether you need a customer support chatbot, document Q&A assistant, or internal knowledge base — a vetted remote AI prompt engineer can have a production-ready RAG pipeline running in 1–2 weeks.
Hire Remote AI Prompt Engineers → Book a Free ConsultationSources & References
- Lewis et al. (2020) — Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Facebook AI Research / arXiv:2005.11401
- OpenAI — Embeddings documentation and pricing: platform.openai.com
- LlamaIndex documentation: docs.llamaindex.ai
- LangChain documentation: docs.langchain.com
- RAGAS — RAG evaluation framework: docs.ragas.io
- Pinecone — Vector database documentation: docs.pinecone.io
- Weaviate documentation: weaviate.io
- Liu et al. (2023) — Lost in the Middle: How Language Models Use Long Contexts. arXiv:2307.03172
Last substantive update: February 2026. Review cycle: Quarterly or when major model releases or tool changes occur.
