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Large language models (LLMs) have transformed natural language processing. They can write emails, summarize documents, and answer complex questions with near-human fluency. But for all their power, LLMs have a few well-known limitations.
They can generate outdated or inaccurate information. They sometimes struggle with domain-specific knowledge. And in fast-moving enterprise environments, they can’t always adapt quickly enough to stay useful.
That’s where retrieval-augmented generation (RAG) comes in. By grounding LLM outputs in external knowledge sources, RAG enhances accuracy, control, and relevance, without the need to retrain the model.
What is retrieval-augmented generation?
Retrieval-augmented generation is a framework that improves how LLMs generate responses. It does this by adding a retrieval step before generation. Instead of answering based solely on what the model “remembers” from its training data, the model first retrieves relevant context from external sources — like internal documentation, knowledge bases, or databases — then uses that information to generate a response.
This process allows the model to incorporate current and authoritative knowledge, improving both factual accuracy and contextual relevance.
How RAG works
At its core, RAG blends search and generation. Here's how the system flows:
- Input: A user enters a prompt. This input could be a question, a task, or a request for information.
- Retrieval: The system retrieves documents or snippets relevant to that prompt. These come from curated knowledge sources and are typically stored in a vector database for fast, semantic search.
- Contextualization: The retrieved content is added to the user’s original input. This gives the model real-time grounding in relevant, trusted material.
- Generation: The LLM uses this enriched input to generate its response. Because it's drawing from retrieved facts, the output is more accurate and contextually aware.
Why RAG matters for enterprise AI
Even the most advanced LLMs can't keep up with every change in company policy, product update, or support process. Static training data eventually falls out of sync with the real world.
RAG addresses this by allowing the LLM to learn in the moment, pulling in the latest knowledge as needed, without retraining.
This makes it especially valuable in enterprise settings where accuracy, timeliness, and trust are critical. Teams get answers that reflect the current state of the business, not what the model was trained on months ago.
Benefits of using RAG with LLMs
RAG brings several tangible advantages to enterprises adopting generative AI:
- Improved accuracy: By grounding responses in real-time information, RAG reduces hallucinations and helps ensure factual correctness.
- Domain-specific expertise: LLMs can tap into company-specific data sources like support docs, internal wikis, or sales playbooks, without custom training.
- Adaptability: As your company evolves, your RAG-enabled system evolves with it. You can update or swap out data sources without touching the model itself.
- Cost efficiency: Because you don’t have to retrain your model every time your data changes, RAG offers a scalable way to improve performance across teams.
- Trust and control: You choose what sources to retrieve from, giving you control over accuracy, tone, and safety, essential for enterprise readiness.
A closer look: How RAG integrates with LLMs
Behind the scenes, a RAG system relies on a few core building blocks:
Indexing external data
To make information retrievable, it first needs to be segmented into smaller chunks (such as paragraphs or QA pairs) and converted into vector embeddings (mathematical representations of content meaning). These embeddings are stored in a vector database that supports similarity search.
Retrieving relevant context
When a prompt is submitted, it's also embedded and compared against the stored vectors. This lets the system surface content that’s semantically related, even if the wording doesn’t match exactly.
Retrieval methods like dense semantic search, re-ranking, or hybrid approaches can be used to improve precision.
Feeding context to the model
The top-matched content is appended to the original prompt. This enriched input is passed to the LLM, allowing it to generate a response that’s informed by current, trusted information.
This process effectively augments the LLM's capabilities, without altering the model’s architecture or training data.
Key considerations when implementing RAG
While RAG is a powerful framework, getting it right requires thoughtful setup and continuous iteration.
Here are some factors to keep in mind:
- Source quality matters: Garbage in, garbage out. Curate your data sources carefully. Poorly written, outdated, or inconsistent content will degrade results.
- Relevance over volume: Retrieve only what’s truly useful. Too much irrelevant context can confuse the model or lead to bloated outputs.
- Stylistic consistency: Pulling from multiple sources can lead to mismatched tones or conflicting information. Make sure your system includes mechanisms to normalize tone and avoid contradictions.
- Balancing retrieval and generation: Your model shouldn't just echo retrieved content, it should use it to generate richer, more nuanced responses. That requires prompt tuning and thoughtful design.
- Evaluation and feedback loops: Regularly monitor response quality and adjust your indexing, retrieval, and generation settings as needed. Continuous improvement is key to long-term success.
Getting started with RAG
You don’t need to build a RAG system from scratch. Most enterprise teams start with a stack of established tools and frameworks, such as:
- Vector databases like Pinecone, Weaviate, or FAISS
- Embedding models from providers like OpenAI or Cohere
- Search orchestration tools to manage ranking and filtering
LLM APIs for final generation
Start by identifying the key documents and data your teams rely on. This might include support FAQs, internal policies, engineering docs, or sales resources.
Then, structure those materials for retrieval: chunk them, embed them, and store them in a vector database. From there, experiment with prompt templates, scoring logic, and evaluation frameworks to fine-tune the end-to-end experience.
Why Glean uses RAG
At Glean, we use retrieval-augmented generation to ensure our AI answers are grounded in your company’s data — so you can trust the results.
Whether an engineer is searching for onboarding docs or a support rep needs policy context mid-ticket, Glean retrieves the right information in real time and uses it to generate helpful, accurate answers. All while respecting access controls and permissions.
RAG helps us keep answers current, personalized, and useful across every department.
Smarter AI starts with smarter context
The future of AI in the enterprise isn’t just about generating better responses. It’s about generating the right ones. RAG offers a practical path forward, turning LLMs into tools that are not only fluent, but also accurate, relevant, and grounded in your business.
It’s not enough for AI to be capable. It needs to be informed. And with RAG, you don’t have to wait for the next model upgrade to get there. You can build systems that grow and adapt with your knowledge.
As more companies explore how to scale AI across teams, the difference between hype and real value will come down to trust and control. RAG provides both. The question now is: what could your teams accomplish if they had the right information at the right time?
Want to see it in action?
RAG helps turn LLMs into enterprise-ready tools. And Glean makes it easy to get started.
If you're exploring ways to bring trustworthy, context-aware AI to your organization, request a demo to see how Glean uses retrieval-augmented generation to connect your teams with the knowledge they need — fast.
