What Is Retrieval-Augmented Generation (RAG)? 2025 Guide

RAG pairs a large language model (LLM) with a search step that pulls in your own knowledge before generation.

RAG shines when you need fresh, factual answers grounded in private or fast-changing data. Fine-tuning shines when you need a model to learn tone, domain language, or workflows that aren't in the base model.

Most real systems blend both: retrieval for facts, small targeted fine-tunes for behavior. If your main pain is hallucinations, compliance, or "the model doesn't know our content," start with RAG. If your pain is "the model can't follow our style/process," consider fine-tuning.

Use hybrid when both are true.

What is RAG? A plain-English definition

Retrieval-Augmented Generation (RAG) is a pattern where an LLM answers a question using evidence fetched from a knowledge source at query time. Instead of asking the model to "remember" everything in its weights, you fetch relevant passages (documents, tables, code, tickets) and pass them into the prompt so the model can cite, summarize, or reason over them.

3 core stages: retriever, augmentation, generator

Retriever: Finds the most relevant chunks of content to a user query. This can be vector search, keyword search, or hybrid.

Let's say an employee asks, "How much vacation time do I have?"

The system doesn't immediately pass this question to the language model. Instead, it searches your organization's knowledge base—in this case, HR policy documents and the employee's personal records.

Using semantic search powered by vector databases, the system identifies the most relevant information based on meaning, not just keyword matches. This retrieval happens in milliseconds, pulling together policy guidelines and the specific employee's accrued time off.

Augmentation: During the augmentation stage, the system combines the retrieved information with the original question to create an enhanced prompt.

Rather than asking the LLM to answer "How much vacation time do I have?" in isolation, the augmented prompt includes the relevant policy document and the employee's record.

The prompt might read: "Based on the following information: [policy document] and [employee record], answer this question: How much vacation time does this employee have?"

This context injection is what separates RAG from standard LLM queries.

Generator (LLM): Composes the final answer using the retrieved context, often with instructions and formatting constraints.

Because the model now has specific, accurate information to work with, it can provide a precise answer with citations.

The employee receives something like: "You have 12 days of vacation remaining for this year. According to the company policy dated [date], you accrue 1.5 days per month, and our records show you've used 6 days so far."

RAG vs fine-tuning vs hybrid — a pragmatic decision framework

If facts change weekly, you have regulated content, or you must show sources, start with RAG. If you need domain-specific reasoning or consistent tone, consider fine-tuning.

Fine-tuning involves retraining a language model on your specific data, adjusting the model's internal parameters to make it better at your domain. This process requires varying cost levels but, depending on the model provider, generally takes time through a strict evaluation process.

That's because the LLM model learns your domain deeply, understanding specialized terminology and producing outputs in your preferred style. A legal firm might fine-tune a model on thousands of case documents, teaching it to write in precise legal language with proper citations to precedent.

RAG, by contrast, keeps the base model unchanged and instead provides it with external information at query time. Implementation of RAG can be done very quickly. The system can incorporate new information immediately by updating the knowledge base, requiring no retraining. Providers like Inkeep, for instance, can get it done in 24 hours or less, thereby affording enterprises speed without risking model accuracy.

Many teams land on hybrid: RAG for grounding + a light fine-tune or instruction-tuned adapter for style and task compliance. The economic angle matters too: retrieval shifts costs toward indexing/storage; fine-tuning shifts costs toward training and versioning. Validate both with a small proof of value.

The cost & latency math

RAG costs come from three places: embedding and indexing content, retrieving at query time, and generating the answer. Latency comes from retrieval hops (search + rerank) and the LLM's token throughput. Caching (answers, prompts, retrieved contexts) can dramatically reduce both.

Indexing: Pay once to embed and store content; re-embed when content changes.

Query time: Vector search + keyword search + rerank + LLM generation.

Caching: Layer responses and contexts; aim for high hit-rates on repetitive questions.

Evaluating RAG (RAGOps) — metrics that move the needle

If you can't measure it, you can't ship it responsibly. Establish an evaluation harness before rolling out.

Groundedness/faithfulness: Does the answer stick to retrieved facts?

Answer quality: Is it correct, complete, concise, and well-formatted?

Coverage/recall: Are we retrieving the right evidence for the query?

Citations: Do links point to the actual source passages?

Real-World RAG Applications

RAG shines brightest in customer support, where organizations need to answer questions using constantly updating product documentation. A perfect example is the "Ask AI" assistant you might see on modern websites—these systems answer questions by retrieving information from documentation and public-facing content in real-time.

The Solana Foundation provides a compelling real-world example of Inkeep's RAG implementation. They used Inkeep to scale developer support without expanding their team, achieving:

Documentation Discovery: The Unified Search feature surfaced previously hard-to-find resources, reducing documentation redundancy and improving overall content utilization.

Support Capacity Expansion: Inkeep "enabled Solana to deliver reliable, scalable developer support without needing to significantly expand the team."

Cost Efficiency: By handling the large share of support needs directly through AI, Inkeep delivered measurable ROI by avoiding hiring additional developer relations engineers.

Is RAG still needed with long-context models?

Yes—for now. Long context helps when you can pack everything into the prompt, but it doesn't replace retrieval. Without retrieval, you still pay to stuff large contexts every time, you risk missing the right passages, and you lack document-level governance and citations. Retrieval narrows context to the most relevant evidence and preserves audit trails.

Conclusion & next steps

Pick a path: RAG for grounding, fine-tuning for behavior, or hybrid for both. Stand up a minimal pipeline with chunking, hybrid retrieval, reranking, and a small eval set. Measure groundedness, answer quality, and cost. Add security and governance from day one. When you're ready, scale indexing and caching, then tune prompts and chunking.

RAG enables AI agents to work with current information while maintaining accuracy and traceability—essential capabilities for enterprise AI customer experience.

How Inkeep Makes RAG Enterprise-Ready

At Inkeep, we've built a RAG platform specifically designed for enterprise needs—combining speed, accuracy, and governance in a production-ready package.

Speed to Value: Go from content to deployed AI assistant in 24 hours or less. Our infrastructure handles ingestion, chunking, embedding, and indexing so your team focuses on business value, not infrastructure.

Hybrid Retrieval Architecture: We combine vector search, keyword search, and intelligent reranking to surface the most relevant context—not just semantically similar passages.

Built for Compliance: Document-level access controls, complete audit trails, and citation enforcement ensure every answer is traceable and governed.

Multi-Modal Knowledge: Ingest documentation, tickets, Slack conversations, code repositories, and structured data. Our system understands relationships across diverse content types.

Continuous Learning: Capture user feedback, surface knowledge gaps, and automatically improve retrieval quality through reinforcement from real interactions.

Whether you're building customer support automation, internal knowledge assistants, or multi-agent systems that require factual grounding, Inkeep provides enterprise-grade RAG infrastructure that scales with your business.