If you're building an AI application that needs to answer questions from your own data, you've likely run into the limits of a bare LLM. It hallucinates, goes stale, and can't cite its sources. A RAG platform solves these problems by wiring retrieval into the generation loop so your model's responses stay grounded in real, current documents.
According to Databricks' State of Data + AI report, 70% of companies using generative AI are now using retrieval tools, vector databases, or external knowledge sources to augment their base models rather than relying on off-the-shelf LLMs alone. The vector database category grew 377% year-over-year as enterprises moved to build RAG applications at scale.
This guide walks through what a RAG platform actually is, how the underlying pipeline works, what to look for when evaluating one, and how to test retrieval quality in production.
What is a RAG platform?
A RAG platform is a system that packages the retrieval-augmented generation pattern into a cohesive product or framework. It handles document ingestion, chunking, embedding, vector storage, retrieval, prompt augmentation, and generation through a single interface. The term sits between two simpler tools, so it helps to draw boundaries after defining the core concept.
The core retrieval-augmented generation concept
Retrieval-augmented generation is a pattern where you fetch relevant documents from an external knowledge base and inject them into the LLM's prompt before it generates a response. Instead of relying entirely on weights frozen at training time, the model gets fresh, authoritative context at query time. The result is output that reflects your data, not the internet's data.
How a RAG platform differs from a bare LLM or standalone semantic search
If you use a bare LLM, it takes a prompt and produces text from its training distribution with no mechanism to consult external documents. Standalone semantic search finds relevant passages but doesn't synthesize an answer. A RAG platform combines both: it retrieves, then generates.
The practical difference matters. If you hand a user raw search results, they still have to read and interpret them. If you use a bare LLM, it will confidently fabricate details it doesn't know. A RAG platform closes that gap by grounding generation in retrieved evidence.
Why RAG platforms matter
You reach for a RAG platform when an LLM alone can't give you accurate, timely, domain-specific answers. Three properties make these systems valuable for production use.
Keeping responses grounded in current, authoritative data
Your training data has a cutoff date. Policies change, product specs update, regulations shift. A RAG platform lets you swap in fresh documents without retraining or fine-tuning the model. The LLM sees the latest version of your knowledge base every time it generates a response.
Reducing AI hallucinations and improving user trust
When you include source passages in the prompt, the model has concrete text to reference rather than inventing plausible-sounding facts. This doesn't eliminate AI hallucinations entirely, but it significantly reduces them. You can also surface citations alongside the generated answer so users can verify claims.
Developer control over knowledge sources and update cadence
You decide which documents enter the index, how frequently they're re-embedded, and what metadata filters apply at query time. This is a level of control that fine-tuning doesn't offer. If a document is wrong or outdated, you remove it from the index. The next query never sees it.
How a RAG platform works
Your RAG pipeline has two halves: an offline pipeline that prepares documents and an online pipeline that handles queries. Understanding both is essential for debugging quality issues.
Ingesting and preparing your data
You start with raw documents: PDFs, Markdown files, HTML pages, database exports. The ingestion pipeline splits each document into chunks, typically a few hundred tokens each, using one of several chunking strategies: recursive splitting, character-based, or token-aware. Chunking directly affects retrieval quality because chunks that are too large dilute relevance, while chunks that are too small lose context.
Each chunk is then passed through an embedding model (providers like OpenAI, Voyage, and Cohere offer APIs for this) to produce a vector: an array of numerical representations that capture semantic meaning. Those embeddings are written to a vector database along with the original text and any metadata.
Retrieving relevant context at query time
When you submit a query, the same embedding model converts it into a vector. The vector database performs a similarity search and returns the top-k most relevant chunks. A hybrid search approach combines vector similarity with keyword search (sometimes called keyword search or BM25) for tighter precision on exact terms and phrases. This combination often outperforms pure semantic search in production because some queries benefit from exact keyword matching rather than semantic generalization.
Augmenting the LLM prompt with retrieved passages
Your system assembles a prompt that includes the retrieved chunks as context, along with the user's question and any system instructions. The LLM generates its response conditioned on this augmented prompt. Prompt engineering choices matter here: putting context before the question typically yields better results than appending it after.
Keeping the knowledge base current
A stale index defeats the purpose of retrieval-augmented generation. You need automated data pipelines that re-ingest documents on a schedule or in response to change events. Batch processing works for most use cases, but if your data changes frequently, you may need near-real-time ingestion with incremental re-embedding.
Key components to evaluate in a RAG platform
When you're comparing RAG platforms, feature lists can blur together. Focus your evaluation on four areas that directly affect retrieval quality and operational flexibility.
| Component | What to evaluate | Why it matters |
|---|---|---|
| Vector store and retrieval | Supported similarity metrics, hybrid search, filtering, scalability | Determines whether you retrieve the right chunks |
| Document ingestion | Supported file formats, chunking strategies, metadata extraction | Affects how well your data is represented in the index |
| Model routing | Number of supported providers, ease of swapping models | Prevents provider lock-in and enables cost optimization |
| Access control | Per-document permissions, tenant isolation, audit logging | Required for enterprise data governance |
Vector store and retrieval quality
Your vector store is the backbone of your RAG engine. Evaluate whether it supports metadata filtering, hybrid search, and tunable similarity thresholds. If you're already running Postgres, pgvector is a solid default. For a new project, standalone options like Chroma, Milvus, or Weaviate are worth evaluating. Pinecone and Elasticsearch address managed and enterprise use cases respectively. For workloads requiring tensor-based ranking and real-time updates at web scale, Vespa is worth evaluating: it unifies vector search, full-text search, and machine-learned ranking in a single engine, and powers Perplexity's web-scale RAG infrastructure.
Document ingestion pipeline and supported formats
Your ingestion pipeline needs to handle the document formats you actually have. Look for built-in support for Markdown, HTML, PDF, JSON, and LaTeX, as well as unstructured data sources like database exports and API feeds. Chunking flexibility matters too: the chunking strategies you need vary by content type. Legal documents benefit from structure-aware splitting; code repositories need different chunk boundaries than prose.
Model routing and provider flexibility
A RAG platform that locks you into a single LLM provider becomes a liability when pricing changes or a better generative AI model ships. Model routing lets you swap providers with a one-line configuration change. This also enables cost optimization: you can route structured extraction tasks to a cheaper, faster model while keeping open-ended generation on a larger large language model.
Access control and data governance
If your knowledge base contains sensitive data, you need per-document or per-tenant access controls. The RAG platform should support metadata-based filtering at query time so users only retrieve documents they're authorized to see. Audit logging is essential for compliance-heavy domains.
RAG frameworks: comparing the major options
Your choice of RAG framework shapes how much pipeline logic you write yourself versus what you get out of the box. The four most widely used options in 2025 take different architectural bets.
| Framework | Architecture | Strengths | Best for |
|---|---|---|---|
| LlamaIndex | Data-framework with 300+ connectors; ingestion → indexing → retrieval → evaluation in one cohesive stack | End-to-end cohesion; LlamaParse for complex PDFs; 40% faster retrieval than custom implementations; Python and TypeScript | rag pipelines centered on complex document processing and multi-source ingestion |
| Haystack | Directed acyclic graph (DAG) pipeline with serializable, auditable nodes | Built-in hybrid search (BM25 + dense), cross-encoder reranking, evaluation nodes, MCP server exposure via Hayhooks; integrates with Elasticsearch, Milvus, Weaviate, Pinecone, **FAISS | Production RAG in regulated industries where auditability and retrieval accuracy are non-negotiable |
| LangChain / LangGraph | Modular chain-and-agent framework; LangGraph adds graph-based state management | 50,000+ integrations; largest developer community; Corrective RAG and Adaptive RAG patterns via LangGraph | Rapid prototyping and multi-step agentic AI workflows that combine retrieval with other tools |
| Vespa | Unified AI search platform combining vector search, full-text search, structured filtering, and ML ranking in one engine | Tensor-based multi-phase ranking; integrated chunking and chunk-level scoring; sub-100ms latency at billions of vectors; real-time index updates; deployed by Perplexity, Spotify, and RavenPack | High-throughput RAG with custom ranking, real-time freshness, and web-scale data volumes |
Common challenges in RAG implementation
You'll encounter predictable friction points when moving from a prototype RAG app to a production system. Knowing the failure modes upfront helps you budget time and engineering effort at the right layers.
| Challenge | What causes it | How to address it |
|---|---|---|
| Retrieval precision and recall | Top-k too high dilutes signal; too low misses relevant chunks | Tune similarity thresholds, top-k, and add a reranking step |
| Chunking and embedding model fit | Wrong chunk size or a general-purpose embedding model producing weak vectors for your domain | Start with 512-token chunks and 50-token overlap; evaluate domain-specific embedding models |
| Latency and cost at scale | Every query triggers an embedding call, a vector search, and an LLM generation; costs compound | Cache frequent queries, batch embedding calls, route simple questions to smaller generative AI models |
| Unstructured data ingestion | Production knowledge bases contain PDFs with embedded tables, scanned images, HTML with boilerplate | Use a document parsing layer that handles format detection, extraction, and normalization before chunking |
Reference architecture for a production RAG application
You can think of a production RAG platform as two pipelines connected by a vector store.
Offline pipeline: ingestion, embedding, and index management
Your offline pipeline runs on a schedule or in response to document change events. It handles format conversion, chunking, embedding, and writing vectors to the index. Metadata extraction (dates, categories, document IDs) happens here too. As part of a well-designed data pipeline, version your index so you can roll back if a bad batch enters the pipeline.
Online pipeline: query rewriting, retrieval, reranking, and generation
Your online pipeline handles user queries in real time. A query rewriting step can expand abbreviations or rephrase vague questions before embedding. After retrieval, an optional reranker applies a cross-encoder or other scoring model to reorder results by relevance. The top-ranked chunks are assembled into the prompt, and the LLM generates the response.
Agentic RAG: when retrieval is a tool call inside an agent loop
You don't have to treat retrieval as a fixed pipeline step. In an agentic architecture, retrieval becomes a tool that the agent calls when it decides it needs external context. The agent might query the vector store, evaluate whether the results are sufficient, and decide to search again with a different query, all within a single turn.
This pattern is especially useful when you need to reason over multiple knowledge sources or combine retrieval with other tools. Mastra supports this pattern by letting you define retrieval as a tool within an agent's toolset, so the agent decides when and how to call it within its loop.
Building a RAG pipeline on Mastra
If you're working in TypeScript, you can build a complete RAG pipeline with Mastra's open-source framework. Mastra's built-in chunking strategies (recursive, character-based, token-aware, and format-specific for Markdown, HTML, JSON, and LaTeX) integrate with vector stores including pgvector, Pinecone, Qdrant, and MongoDB through a standardized API. Mastra's model routing connects to hundreds of models across dozens of providers through a single interface, so you can run embedding on a cost-optimized model and generation on a larger one without changing your pipeline code. Retrieval evals are built in, letting you measure precision, recall, and faithfulness as part of your CI pipeline.
You define your ingestion pipeline as a workflow, chain embedding and indexing steps with .then(), and expose the vector store as an agent tool. When the agent decides it needs external context, it calls the tool. Mastra's runtime handles the round-trip and drops retrieved chunks into the LLM prompt. Hybrid queries combine vector similarity with metadata filtering out of the box. Every query, embedding lookup, and rerank step is logged as an OTel span, so you can trace exactly which chunks were retrieved and whether the model used them. Here is the core of an agentic RAG setup:
Testing, observability, and evaluating RAG quality
Your RAG pipeline will produce bad answers sometimes. The question is whether you detect that before your users do.
Retrieval evals: precision, recall, and NDCG
When you evaluate retrieval quality, track all three of the following metrics across a labeled eval dataset to catch regressions when you change chunking or embedding models. Frameworks like DeepEval and RAGAS provide the tooling to compute them at scale.
| Metric | What it measures | Why it matters |
|---|---|---|
| Precision | Fraction of returned chunks that are actually relevant | High top-k without a reranker inflates irrelevant results |
| Recall | Fraction of all relevant chunks that were returned | Too-low top-k causes missed evidence |
| NDCG** (Normalized Discounted Cumulative Gain) | Ranking order, penalizing relevant results that appear too low | A relevant chunk on page three is worth less than one at position one |
Generation evals: faithfulness, answer relevance, and groundedness
For generation quality, use LLM-as-judge: pass the output, the input, and the retrieved context to a judge model with a rubric. The three metrics to track are:
| Metric | What it measures |
|---|---|
| Faithfulness | Whether the generated answer is supported by the retrieved passages |
| Answer relevance | Whether the answer actually addresses the question asked |
| Groundedness | Whether every claim in the output traces back to a specific source chunk |
Tracing retrieval and generation steps in production
You need visibility into what your pipeline is doing on every query: which chunks were retrieved, what scores they received, what prompt was assembled, and what the model returned. OpenTelemetry-based tracing gives you this as a tree of spans. Mastra's tracing surfaces these details in local development through Mastra Studio and exports OTel-compatible traces for production monitoring.
Guardrails and output sanitization
Your RAG pipeline can surface sensitive data if the retrieved documents contain it. Output guardrails screen generated responses for PII leakage, off-topic content, and prompt injection artifacts. Input guardrails check incoming queries for adversarial patterns before they reach the model. Both guardrail layers are standard practice across production RAG tools.
The future of RAG technology
Your current RAG pipeline isn't the final architecture. The decisions you make now will determine how much you need to rebuild in a year.
Long-context models vs. RAG: complementary or competing?
Models with context windows approaching one million tokens raise the question of whether you can skip retrieval entirely. In practice, the right answer depends on your document corpus size. For small, stable knowledge bases that fit within 50K to 100K tokens, dropping everything into context is a reasonable shortcut. For larger corpora, RAG remains the better approach: model performance starts degrading meaningfully as prompts grow past 100K to 125K tokens, and the cost of sending your entire knowledge base on every query scales poorly. RAG keeps the context window focused on the most relevant passages, which is why it stays the dominant architecture for production applications with large or frequently updated document sets.
Hybrid approaches: RAG inside multi-agent and agentic AI workflows
You'll increasingly find RAG embedded inside agent architectures rather than standing alone. An agent supervisor might dispatch a retrieval subagent to search a knowledge base, a code-execution subagent to process the results, and a synthesis agent to draft the final answer. This modular approach lets you evaluate and improve each component independently.
Emerging trends: multimodal retrieval and graph-augmented RAG
If your data includes images, diagrams, or structured relationships, multimodal retrieval extends RAG beyond text. Graph-augmented RAG adds entity-relationship structures on top of vector search, enabling queries that follow connections between concepts rather than relying purely on semantic similarity. Both are worth tracking if your data is richly structured.
A RAG platform gives you a structured way to ground LLM responses in your own data, but the quality of your pipeline depends on the choices you make at each layer: chunking, embedding, retrieval, and evaluation. Get those components right, instrument them with tracing and evals, and you have a system that stays accurate as your knowledge base evolves.
