AI’s power is premised on cortical building blocks. Retrieval-Augmented Generation (RAG) is one of such building blocks enabling AI to produce trustworthy intelligence under a given condition.

RAG can be described as a technique that enhances Large Language Model (LLM) outputs when AI performs tasks and delivers outputs basing its thinking and building capacities on relevant, up-to-date data from external, trusted sources which include organisational documents, knowledge base or database before generating a response. Through, RAG, AI responses are informed by evidence which can be trusted because it is anchored on a dependable source of truth.

In relying on AI output a recurring challenge has been the issue of hallucination. AI hallucination describes a situation when an AI system confidently produces information, which on the face of it may sound plausible but is oftentimes completely inaccurate, misleading, not grounded in a real evidence and impossible to verify. This happens when AI pulls from sources that are not trusted and which contain fake claims etc.

In other words the problem of hallucination in AI arises because LLM models works through prediction that is based on word patterns than knowing truth, hence if AI training data is biased, incomplete or erroneous, AI will still go ahead and generate outputs from such datasets since it has the tendency to fill gaps by making things up in the absence of reliable data.

AI hallucination has long lasting impact since it fuels misinformation, undermines trust and more importantly leads to bad decisions especially when it comes to issues such as health, finance or public policy. Think of it this way, someone with a health condition may rely on a false information in deciding on health treatment options due to AI hallucination.

Given this context, RAG can help in providing trustworthy intelligence since it provides better inputs and provides sources thereby improving accuracy. The beauty of this, is that RAG does all this without the need to retrain the underlying model reinforcing the notion that solid building blocks lead to better outcomes.

How Does RAG Work?

RAG works typically through a three -step process to ensure its output are anchored on relevant data, information or knowledge. The first step is “retrieve”. As the first step in this process, a user usually asks a question and based on the users’ prompt, the system does an extensive search on an external database, with the view of retrieving relevant and context-specific outputs in line with the user’s question.

This output is used for the second step; augment. In this second step, the retrieved output is added to the users’ original question (prompt) situating it within the necessary context. This step is very crucial because without it, the LLM would generate answers by relying only on its internal training data, resulting in the generation of inaccurate information and hallucinations.

The Augment phase of RAG, provides the LLM with external, up-to-date, verifiable contexts, thereby radically reducing hallucinations while maintaining fluency of outputs. The process is completed with generate, which is the culmination of the processing. Here the LLM uses this augmented prompt to produce relevant, accurate, context-specifics outputs.

The import of this being that the raw data retrieval is not useful, as it may be verbose and fragmented which requires the generate step to take this raw data and put it into concise summaries in a natural language, tailored to the user’s exact question.

Through complex reasoning and the synthesization of insights from multiple retrieved documents and the removal of elements which are not relevant to the users’ question or context RAG converts outputs from machine-readable sources into seamlessly human-readable truth.

There are several use cases for RAG, the popular ones include building enterprise-grade AI applications such as customer support chatbots, which are trained on relevant datasets like product manuals, and internal policies. This leads to the generation of more personalized, accurate answers, which means the customer will not feel like they are served by a dumb machine but are provided with context rich answers. Another important use case is organisations who have a large pool of internal documents which they need to generate accurate insights from, through RAG, retrieval capabilities, accessing specific information is done seamlessly.

Key Benefits of RAG

The benefits of RAG are numerous including producing accurate and reliable outcomes devoid of high levels of hallucinations since answers provided on premised on relevant, factual and up-to-date information. Also, another powerful benefit of RAG is the generation of contextually relevant outputs since it empowers models to use specialised and sometimes private data which is not available in its initial training data set.

RAG can ensure that LLM do not have to undergo expensive and frequent retraining since it can rely on updated knowledge bases, making it a more cost -effective option. Further, the power of RAG means more transparent and explainable outcomes since the AI models provide their data source leading to trust in the output.

Key RAG Challenges and Limitations

Though RAG comes with a lot of benefits, it is not devoid of challenges and limitations which could include retrieval failure (that is relevant or missing content), poor context and data quality negatively impacting on the performance of RAG, a very high operational cost since there is the need to maintain, update and index an extremely large and dynamic dataset requiring substantial infrastructure and compute resources. RAG can also suffer from security and privacy issues as well as evaluation difficulties in terms of how-to measure overall effectiveness.

However, some of these challenges could be resolved using SQL RAG, (which can provide precise results), GraphRAG, (using knowledge graphs), agentic chunking (breaking text in semantically meaningful chunks) and governed data lakehouses(unstructured data ingested into multiple databases).

In conclusion, RAG is an important solution to improve the performance of LLM while reducing hallucinations, however, it suffers from some limitations and challenges. If it is going to transform the future of trustworthy intelligence and contribute to more accurate generated outputs to user questions, then there is a need for government and companies to invest in LLM training to resolve these challenges and limitations.