Unstable AI? How to use RAG to anchor answers

The problem of fabricated answers in artificial intelligence

Large language models, popularly known as LLMs, impress users with the fluency of their writing. In practice, however, they operate as sophisticated textual probability generators rather than infallible databases. When an artificial intelligence does not know a specific fact, instead of admitting ignorance, it often manufactures a convincing answer, a phenomenon known as hallucination. To mitigate this unwanted behavior in production environments, engineers rely on search and retrieval architectures, combining traditional lookups with text generation.

When combining this strategy with additional security mechanisms, as explained in our article about how to use guardrails for LLM outputs, we can build robust and predictable systems. In practice, the secret lies in not trusting the model's internal memory blindly, but rather feeding it the correct facts at the exact moment the user asks the question.

What is RAG and how the architecture works

RAG stands for Retrieval-Augmented Generation. Simply put, it is an architectural pattern where the system intercepts a user query, searches for relevant documents in an external database, and passes those texts along with the original question to the language model. Thus, the artificial intelligence receives an open reference book on the table and answers strictly based on those pages rather than its vague recollections.

This approach solves a classic dilemma in modern software engineering: language models have training cutoff dates and do not know internal corporate data or recent updates. By injecting the appropriate context into the API call, we allow any software to utilize private information with surgical precision, maintaining complete control over which data sources support each generated answer.

Splitting documents into intelligent chunks

The first practical step in building a RAG pipeline involves preparing the knowledge base. Long corporate documents, such as three-hundred-page technical manuals, do not fit entirely into an AI context window without losing crucial details. Therefore, we split the files into smaller pieces, a technique called chunking. Each fragment typically contains between three hundred and five hundred words, ensuring that autonomous meaning is preserved.

Beyond size, it is essential to manage overlaps between consecutive blocks to ensure important sentences are not cut in half. To organize complex manipulation workflows for these data before sending them to storage, many developers rely on specialized libraries, a topic we cover in detail when discussing how to structure flows in LangChain to maintain code readability and maintainability.

Transforming texts into mathematical vectors

After slicing the documents, we need to make them searchable by meaning rather than just exact keywords. To achieve this, we use embedding models that convert each text block into a long list of numbers called a vector. In practice, these numbers represent the semantic position of the concept in a multidimensional space, where similar ideas stay close to one another.

When a user types a question, the system converts that query into a vector using the same mathematical model. Next, a vector database performs a proximity search to find the fragments whose numbers align most closely with the query. It is precisely this mechanism that allows the system to understand that the word vehicle might relate to car or automobile, even without repeating the exact spelling in the original text.

Filtering and reordering retrieved results

Finding the closest documents is not always enough to guarantee a perfect answer. Often, the vector database retrieves ten promising snippets, but only two contain the exact answer while the others bring lateral noise. To solve this, we apply an additional step called reranking, using a smaller model specialized solely in scoring the actual relevance of each snippet to the specific question.

This intermediate filter removes distractions and ensures the artificial intelligence receives only the highest quality information, reducing processing costs and increasing final response accuracy. Validating the quality of these generated responses automatically is the next logical step to ensure stability, as discussed when exploring how to build tests with Ragas in high-demand environments.

Final considerations on data grounding

Implementing a retrieval-augmented generation architecture turns volatile language models into reliable, auditable corporate assistants. By shifting the burden of data verifiability from the model's internal memory to a controlled external repository, we eliminate most hallucinations and ensure every claim can be traced back to its original source. The success of this endeavor depends on a continuous cycle of tuning chunk sizes, selecting search models, and rigorously monitoring production outputs.