How to Use Retrieval‑Augmented Generation to Prevent Hallucinations

The quest for precise answers in AI systems still faces the classic problem of hallucinations: the model generates text that sounds plausible but has no basis in real data. The Retrieval‑Augmented Generation (RAG) technique emerged as a practical answer to this challenge, allowing AI agents to consult external sources before producing the final output. In this post we will detail how RAG works in practice, which architectural decisions influence answer reliability, and how to apply these principles in on‑demand projects with the Build Together approach from VertexHub.

What is RAG?#

RAG combines two distinct steps:

1. Retrieval – the agent queries a document index (e.g., knowledge bases, PDF files, API logs) using a query generated from the user’s question.
2. Generation – the language model receives, in addition to the original prompt, the retrieved content and generates the final answer.

The separation makes it clear that generation does not rely solely on the model’s internal “memory”; it has access to up‑to‑date, verifiable information. This architecture aligns with our AI‑first / agentic‑first positioning, where intelligence is not an add‑on resource but an integral part of the solution from the first commit.

Why can pure generation “invent”?#

Language models trained on large corpora learn language patterns but do not have direct access to external databases at inference time. When they receive a question that exceeds their internal knowledge, they:

• Interpolate information from similar contexts, producing answers that seem correct but lack factual backing.
• Compose text fragments based on probabilities, which can lead to non‑existent combinations (e.g., citing an article that was never published).
• Lack updates: knowledge is frozen at the training cut‑off date, so recent facts are missing.

These limitations are the root of hallucinations. Without an external verification mechanism, the model cannot distinguish between “I know” and “I don’t know.”

How AI agents use RAG in practice#

1. Defining the retrieval corpus#

Choosing the document set is the first quality‑control point. In typical projects, the corpus may include:

• Product manuals and API documentation.
• Internal knowledge base (FAQs, support tickets).
• Event records or logs that describe real system behavior.

Indexing should be done with tools that support semantic search, such as text embeddings, so matching goes beyond exact keywords.

2. Building the retrieval query#

Before searching, the agent transforms the user’s question into an optimized query for the search engine. Common strategies:

• Reformulation: simplify or expand the question to improve semantic coverage.
• Contextualization: include metadata (e.g., client ID, timestamp) that helps filter relevant results.

This preprocessing layer is usually implemented as a small language model or a deterministic rule set.

3. Context fusion and generation#

With the retrieved documents, the agent assembles a prompt that includes:

• Clear instructions: “Base your answer only on the information below.”
• Evidence block: relevant excerpts from the document, cited with reference (e.g., “According to manual v2.3, page 12…”).
• Size limit: truncate or summarize documents to fit within the model’s token limit.

The generation model, receiving this prompt, produces the final answer while maintaining a reference to the retrieved content. This flow ensures the output is traceable to a concrete source.

4. Post‑processing and validation#

Even with RAG, it is prudent to validate the answer before delivering it to the user:

• Consistency check: compare the generated answer with the original retrieved text to detect divergences.
• Confidentiality filtering: remove sensitive information that should not be exposed.
• Audit log: record the query, the documents used, and the final answer for traceability purposes.

Strategies to Reduce Hallucinations#

Strategy	How it works	Benefit
Explicit prompting	Include instructions like “Answer only based on the excerpts below.”	Reduces the model’s tendency to “invent” details.
Limit number of documents	Select only the top‑k most relevant results.	Avoids information overload and reduces noise.
Re‑ranking	Apply a classification model to order the retrieved documents before generation.	Increases the chance of using the most pertinent source.
Human feedback	Human reviewers evaluate sample answers and adjust search or prompt parameters.	Introduces a continuous improvement loop.
Periodic index updates	Re‑index documents whenever there are relevant changes.	Guarantees the agent works with current data.

These practices are part of our technical DNA: we prioritize solutions that can be audited and iterated quickly, without relying on promises of “perfect AI.”

Integrating RAG into Build Together Projects#

When a client brings a challenge that requires reliable answers – for example, a technical support assistant, a compliance consultant, or an internal search engine – the Build Together approach incorporates RAG from the Architecture phase:

1. Alignment – we identify which knowledge sources are critical and define update requirements (re‑indexing frequency, retention policies).
2. Architecture – we select the semantic search stack, the generation model (usually a general‑purpose LLM adapted to the domain), and integration points with the client’s existing systems.
3. Co‑construction – we deliver increments that include the retrieval layer, integration tests that verify query‑to‑document matching, and CI/CD pipelines that ensure corpus changes do not break generation.

By applying RAG, the client gains:

• Accuracy: answers based on official documents, reducing the need for later corrections.
• Transparency: each answer can be accompanied by its source, facilitating audits and compliance.
• Scalability: the same architecture can be extended to new knowledge domains without rewriting generation logic.

If your business depends on correct information – whether in customer service, risk analysis, or process automation – consider a custom RAG project. Our team is ready to map your sources, build the retrieval layer, and integrate generation in a secure, auditable way.