Large Language Models (LLMs) have come a long way in the past few years, but they are still known to make mistakes, even when they have billions of parameters for guidance, and all the latest training data at their disposal. This results in answers that sound confident, but are not based on facts.
This tends to happen because LLM’s knowledge is based on the training data, which is incomplete, and often outdated. On top of that, it is never fully aligned with the exact information the user needs. Even the best models will fill any gaps in knowledge with guesswork, rather than admitting that it doesn’t know.
These are known as hallucinations, and for businesses, they can create real risks. Their compliance teams worry about inaccurate data concerning policies, legal and HR teams deal with answers that may violate company’s internal rules, and technical teams can see models simply make up product details or use old information, simply because they don’t have access to the firm’s updated documents. Obviously, these are no minor errors, but issues that break trust, slow adoption, and push companies toward adding a human review layer, which defeats the purpose of automation.
Enter Retrieval-Augmented Generation (RAG), which has become a practical fix. Instead of simply relying on what the model remembers, RAG allows companies to attach an external information bank that provides updated data to the model to draw from whenever it tries to answer a question. That way, the model has access to real, updated data and can provide better answers.
However, it is worth noting that RAG is often not enough, as even Retrieval might miss important information, or the model might misunderstand the context, which is where RAG chunking strategies come into play.
The Real Reason LLMs Hallucinate, and Why RAG (Alone) Doesn’t Fix It
To start, let’s address the core issue - LLM hallucinations. They tend to happen because models do not act as fact-checking systems, but rather as prediction systems. In other words, when they lack information, they try to predict what the answer will be based on statistical probability. RAG helps by providing more information and trying to fill that gap.
However, this is not enough, as it still leaves gaps - maybe smaller than before, but they manage to persist. A basic RAG setup will provide dense embedding search, simple chunking, and no evaluation, but dense embedding search tends to be one of the biggest problems. Embeddings try to capture semantic meaning but their flaw lies in often missing exact keywords or technical terms. Because of that, embedding-only retrieval struggles with specific pieces of information, like product codes, error messages, legal clauses, and alike.
So, if the retriever fails to provide the necessary information, the model won’t be stopped by that - it will still try to make up an answer.
RAG document chunking techniques were added as the solution’s next layer. Basically, RAG systems try to cut documents into smaller pieces to make it easier for the model to understand. However, they cut the pieces without considering structure, so a paragraph might be split mid-sentence, or related sections might be split apart and treated as separate things. Once again, the model will see half a paragraph as finished, and try to make up the missing pieces on its own.
Basic RAG also lacks evaluation, as most systems don’t measure the precision of retrieval, nor hallucination rates, or the quality of provided context. Without this kind of monitoring, businesses simply can’t know when the retrieval starts failing, and the embeddings drift. As such, they also can’t know when the model is answering based on data, and when it is going outside of the source material on its own.
Finally, it is worth noting that a lot of RAG examples that are circulating online these days are only toy prototypes, offering very small datasets, with weak search configuration, and usually non-existent production monitoring, so businesses should be wary when applying them.
Modern RAG Architecture That Eliminates Hallucinations
Older RAG systems often aren’t good enough to stop hallucinations, as they rely on simple techniques and expect the model to figure it out on its own, which usually doesn’t happen as intended, and the model still turns to hallucinations to fill in the gaps. This is why a modern RAG was invented, as it works better since it combines several safety layers together, which - if combined correctly - tend to provide better results.
The entire process starts with the way information is retrieved. Instead of relying on a single search method, modern systems use two at the same time. One looks for exact wording and closest matches, while the other tries to match the meaning and context. With both working at the same time, the system is able to accurately retrieve more complete information, and it doesn’t have to turn to guessing.
It is also worth noting that the information is not retrieved as a whole, but rather, it is broken up into chunks. Modern RAG splits documents into smaller, more precise pieces, which ensures that the details don’t get lost or ignored by the model. Meanwhile, they are grouped together so that the model will understand the overall context and understand the topic that it is expected to tackle. That way, it is prevented from mixing unrelated facts and inventing ways to connect otherwise unrelated information.
After the data is retrieved, the system also uses a solution called the reranker, which, as the name suggests, ranks the results by usefulness, and then presents it to the LLM. The most relevant information is prioritised, so that the model doesn’t get overwhelmed with a massive information dump.
However, that is not the end, as the model can still make mistakes, which is why modern systems also monitor themselves. They track whether the answers are supported by the source material, whether information is drifting over time, and if the responses are starting to lose speed and reliability. The system conducts automated tests, which allows the teams to spot such problems early.
Lastly, the systems are also carefully managed, using specialised databases where knowledge is stored efficiently. This is where the systems store common answers so that they don’t have to go through the full process each time, which reduces the strain on the system.
In diagram form, it looks like this:
Dual-retriever RAG pipeline showing BM25 → embedding search → fusion → reranker → LLM.
The №1 Hidden Factor in RAG Accuracy
While all of the mentioned aspects of the RAG system are all important, one that stands as the deciding factor is how you slice the source material. In other words, chunking has the power to make or break the whole system. It decides whether the model will get to see the right facts, and whether it will have to start hallucinating to fill in the gaps.
This is why the best systems do not only use a single method, but try to switch RAG chunking strategies based on the content and the task at hand.
Now, when it comes to RAG document chunking best practices, Small2Big and ParentDocumentRetriever work when precision and context need to coexist. This method retrieves small, tight chunks to achieve greater accuracy. However, you must also attach their parent sections, so that the model can stay aware of the bigger picture and the surrounding logic. This is best used for policy docs, manuals, compliance materials, and alike.
Then, there are adaptive chunking sizes, where each piece is based on structure, such as headings, paragraph breaks, semantic boundaries, and alike. They are uneven, but can offer more complete data to the model, and are generally the safest option to be used as a default when you can’t accurately predict what the user will ask.
Entity-based chunking tends to group text around people, products, APIs, and the like. In other words, it is based around named entities. This makes it best for knowledge bases, product catalogs, and CRM data, where the user’s question involves specific things.
Next, there is topic-based chunking, which groups the entire corpus first, and then creates chunks inside that group. That way, the model is prevented from jumping between topics. Finally, there is task-aware chanking, which matches the chunking to the job. Specifically, long chunks are used for summarization, while smaller snippets work best in Q&A. If the question can help define the answer, this method can reduce the risk of hallucinations.
Each of these types of chunking in RAG serves a specific purpose, and knowing which is best applied for which purpose is half the job in ensuring that the model won’t turn to hallucinations to cope with the task.
Which chunking approach works most effectively in RAG systems?
The optimal chunking strategy depends on the task and document type. Adaptive methods, such as Small2Big, topic-based, or entity-aware chunking tend to balance precision and context preservation, which reduces hallucinations.
What are the different chunking methods used in RAG?
Common approaches include fixed-size chunking, adaptive chunking based on semantic or structural boundaries, Small2Big with parent context, entity-based grouping, topic-based clustering, and task-aware chunking, specifically made for summarizing and Q&A. Simply put, each of these methods serves a specific purpose, but they all work toward increasing retrieval quality and grounding LLMs.
Hybrid Retrieval
Hybrid retrieval in RAG is a technique that combines multiple retrieval methods. Usually, that includes vector search and keyword search, which, when combined, improve the accuracy and relevance of information that the model presents to the user.
Hybrid retrieval typically combines two search styles, which allows the system to find both exact match for the keyword in question, as well as information that is relevant to the keyword used. Simply put, there is sparse retrieval and dense vector search, with the former being used to find the documents that use the exact word, and the latter finding passages that mean the same thing, even if the wording may be somewhat different. Combining the two will result in fewer blind spots, thus ensuring that the model doesn’t have to turn to hallucinations to fill in the gaps.
BM25 is a proven ranking function that uses a scoring system to measure the relevance of the document by keyword overlap. It is also adjusted for document length and frequency of terms in the text. This lets it catch precise identifiers and codes that embeddings can miss otherwise.
Dense vector search, on the other hand, turns text into vectors and retrieves information based on their semantic similarity. It works great with paraphrasing, synonyms, and the like, where the exact words may not be present, but the meaning is.
Then, there is Reciprocal Rank Fusion, or RRF. This is a simple but robust fusion method that ranks lists from each retriever and combines them by summing up inverse-rank scores. Passages that appear near the top in multiple lists are selected, and so it doesn’t depend on a single retriever.
RRF can also be extended further using weights. For questions that are domain-sensitive, the user might upweight BM25, while conceptual questions will see them upweight dense results. Weighting essentially acts as a small knob, which can improve the precision when properly tuned.
Diagram: Parallel sparse/dense retrieval → RRF fusion → top-k reranker → generator
Evolution Frameworks: Correct Measurements of Hallucination Reduction
Modern RAG systems only work if they are measured correctly, which is why evaluation matters. Without it, organisations won’t know whether retrieval is getting better, worse, or if it is silently drifting. Fortunately, reliable evaluation frameworks can help detect hallucinations early and compare the performance of different retrievers. On top of that, they can even help teams understand if chunking strategies are producing the right grounding or not.
This is done through a number of processes, including Retrieval Precision@k, which measures how many of the top-k retrieved documents are relevant to the question being asked. If precision is low, the model receives weak or incorrect context, and that increases the chance for it to turn to hallucinations for answers. Ultimately, this is one of the simplest and most reliable indicators of whether the retriever is doing its job.
Then, there is context relevance scoring, which examines whether the retrieved text supports the answer. Usually, it is calculated by comparing the question and the retrieved text via semantic similarity or cross-encoder scoring, with the goal of checking if the model’s answer is based on evidence or unrelated fragments.
Another aspect that is measured is the hallucination rate. This helps teams measure how often the model produces statements that cannot be traced back to the context provided to it to draw an answer from. It is done by asking the model to cite the source passage, or by using a verification model to check if the claims appear in the retrieved documents or not.
Next, there is a faithfulness vs factuality metric, where faithfulness refers to how closely the model sticks to the retrieved source, while factuality measures whether the statements are true in the real world. This can lead ot various combinations, such as the model being factual but unfaithful, in which case the answer is correct, but not grounded in the retrieval. Alternatively, it can be faithful but not factual, meaning that it was drawn from the (likely outdated or incorrect) retrieved text, but it doesn’t work in a real-world environment.
Another process revolves around using synthetic evaluation sets. Essentially, since creating human-labelled datasets for every domain is not very practical, teams will instead create synthetic evaluation sets via LLMs. They consist of questions, reference answers, and relevance labels. Synthetic sets can even allow teams to run daily or even hourly tests and try to detect retrieval drift, and other inconsistencies.
Finally, evaluation can also be done through benchmarking chunking and retrievers. Essentially, evaluation frameworks test different configurations such as:
- Small vs. large chunk sizes
- Sparse vs dense vs hybrid retrieval
- With and without reranking
- Different similarity metrics
The idea is to compare performance side by side, which allows teams to see which architecture results in the lowest hallucination rate.
Production Deployment Patterns
When a RAG system’s development reaches a stage where the system is deemed reliable, the next challenge lies in deploying it in such a way that it can handle the workloads. Essentially, production deployments need to focus on stability, low latency, and continuous monitoring, and the architecture needs to be sturdy enough and support regular updates.
To operate reliably in production, RAG systems require carefully designed deployment patterns, which are used to define how components are structured, the way data flows between them, and how performance and correctness are maintained at scale. The key areas to consider include architecture patterns, choosing vector DBs, deployment stack, CI/CD for embeddings and chunk updates, and observability.
Architecture patterns include:
- Stateless RAG Microservice: Most organizations deploy it, and the service receives a query, performs retrieval, and runs the LLM call, then returns a grounded answer. Since it is stateless, multiple instances can be added or removed without coordination.
- RAG Caching Strategies: Caching reduces retrieval time and model costs. Typically, there are three types of caches that are often used together, including vector cache (stores common query embeddings and most similar results), answer cache (saves full answers for FAQ), and reranker cache (Stores reranker scores for common document pairs)
- Horizontal Scaling with vector DB sharding: As document collections increase, vector searches become slower, and systems tend to address this by sharding the vector database. Sharding splits the index into smaller pieces that can be searched in parallel, which is something that modern vector DBs handle automatically.
Choosing a vector database can offer different advantages, including:
- Weaviate: Strong hybrid search support, built-in classifiers, and modular components
- Pinecone: Fully managed service featuring predictable latency and strong scaling guarantees
- Milvus: Open-source with high recall and good performance with large collections
- Redis: Simple architecture, good for smaller firms and ultra-low-latency workloads
Ultimately, the choice depends on data size, expected traffic, and whether a team prefers managed or self-hosted infrastructure.
A deployment stack would look something like this: FastAPI + LangChain + Weaviate + OpenAI/Anthropic
This setup allows fast iteration, clear separation of components, and integration with most monitored and CI/CD systems. Speaking of CI/CD systems, document collections change over time, so embeddings need to be kept up to date. This is why production systems include pipelines that detect modified or new documents, re-chunk the existing ones, recompute embeddings, validate retrieval quality, and push updates to the vector DB. That way, the system is prevented from drifting away from new data, and it maintains low hallucination rates.
Finally, there is the matter of observability, which matters because operational visibility is essential in order to know if the model is operating correctly. Production RAG systems keep track of query traces, retrieval logs, and hallucination alerts, all of which, combined, help teams catch problems before they can affect users.
Performance Benchmarks
The way a RAG system is designed has a direct impact on speed, accuracy,and how often it will start making things up to make up for the missing data. Different choices tend to trade performance for quality in different ways.
For example, when it comes to retrieval, systems that rely only on embeddings might work fine when it comes to general questions, but they will encounter issues when it comes to numbers, rare terms, or even the exact wording. This can be fixed by combining search based on meaning and matching based on keywords. The system will find more relevant information and reduce knowledge gaps and blind spots. While there is a small impact on speed, it is usually not too bad and can be tolerated.
Another thing that matters here is the chunking strategy, as simple fixed-size chunks are easy to implement, but the tradeoff is that they split ideas in the wrong places. More advanced chunking starts with small and meaningful pieces, which then get merged together to form complete sections. That way, the context stays intact, while the relevance of information is improved.
This is also why retrieval type plays a large role, as semantic-only retrieval works at greater speeds, but it is narrow and not as reliable. Hybrid retrieval can resolve this issue by combining multiple search signals, improving coverage, and working more reliably across various questions. Again, the trade-off is speed for greater consistency and accuracy.
Finally, the way information is ranked has the biggest impact on answer quality, which is why reordering the results carefully before they are presented to the model ensures that the precision of answers will be increased, while hallucinations are reduced.
Conclusion
Large Language Models are powerful, but they often turn to guessing when they don’t have the right information, choosing to fill in the gaps, rather than admitting that they don’t know the answer. That way, their performance remains more consistent, but the final answer they provide is not accurate, leading to hallucinations.
RAG was created to fix this by feeding models real, updated data, and preventing it from turning to guessing, but the solution is not perfect. Older RAG systems suffer from weak retrieval, poor chunking, and they did not have additional safety measures, such as methods of checking if the model is staying on track, which is why their performance fell short.
Modern RAG systems have learned from these mistakes, adding additional safety layers, like monitoring, and improving the old processes. Together, the new mix of solutions works significantly better. Moving forward, RAG is expected to continue to evolve, but for now, modern RAG works reliably and can be offered to clients who need to improve LLMs’ performance.
