In enterprise settings, AI teams often need to build custom agents to serve domain-specific use cases. Retrieval-Augmented Generation (RAG) is critical to developing these agents, incorporating internal data into their reasoning process and grounding answers in more contextually relevant information. But to deliver truly exceptional performance, you need more than RAG. Your agents need to be specialized for their particular use case, providing the accuracy and reliability required for production deployment.

Specializing RAG agents delivers impressive results. In real-world enterprise deployments across complex domains, specialized RAG agents built on the Contextual AI Platform have achieved nearly 30% greater accuracy than traditional RAG-only systems built with leading frontier models like Claude 3.5 Sonnet and GPT-4o.

Specialization typically entails fine-tuning, prompt engineering, and hyperparameter adjustments to optimize performance. Let’s take a closer look at the specialization process in practice.

 

Specialization via fine-tuning

RAG and fine-tuning are often incorrectly presented as mutually exclusive solutions to customizing an AI system. In reality, organizations can achieve the best results by combining the approaches rather than treating them as an either-or choice.

Fine-tuning an agent helps the AI system learn where to focus its attention and how to better interpret domain-specific information, but fine-tuning often fails to actually inject new knowledge. When fine-tuning and RAG are combined, the approaches are mutually reinforcing: RAG provides the up-to-date knowledge, while fine-tuning optimizes how that knowledge is processed and applied.

Fine-tuning works by adapting the weights of your RAG agent’s components to your domain or business use-case. For the retriever and generator components of your agent, fine-tuning commonly involves passing in a domain-specific fine-tuning dataset of gold-standard queries, responses, and supporting evidence. The agent learns from gaps between its generated responses and the gold-standard counterpart, strengthening its ability to retrieve and reason with your data.

Consider a scenario where a RAG agent is built for a fictional investment management firm, Stonemason Capital. The agent is asked: “How did healthcare companies in the SCMR PE portfolio perform last quarter?”

A standard RAG agent might take a surface-level approach—scanning its documents for the exact phrase “healthcare companies in SCMR PE portfolio” and summarizing any loosely relevant information it finds. This approach often leads to incomplete or generic answers because the agent lacks deeper financial reasoning.

A specialized RAG agent that has been fine-tuned, however, applies domain-specific intelligence to deliver a far more accurate and insightful response. It takes structured steps, such as:

1. Leveraging its understanding of tribal knowledge and jargon: The agent knows SCMR PE is the “Stonemason Consumer, Medial & Retail private equity” portfolio and retrieves the list of portfolio companies to filter out healthcare companies.
2. Applying domain-specific reasoning: The agent knows financial performance is tracked in earning reports and retrieves 10-Q filings of the identified companies.
3. Adheres to expected output format: The agent knows to structure answers based on guidelines in the fine-tuning dataset and includes specific numbers where applicable.

Fine-tuning leads to more accurate and relevant responses, and is thus highly recommended for production use cases. Beyond the retriever and generator, you can also fine-tune other components of the agent, such as improving the reranker’s performance by fine-tuning it with challenging hard negative examples.

 

Specialization via prompt engineering and adjusting agent parameters

Beyond fine-tuning, there are several other methods to specialize your agent for your use case. Prompt engineering of the system prompt is a particularly powerful and effective way of guiding your agent’s retrieval and reasoning to match your requirements. By specifying instructions that you want your agent to follow, you can directly influence the steps that the agent takes when generating a response.

You can also experiment with adjusting hyperparameters controlling various components of the RAG pipeline, such as the retriever, reranker, and generator. For instance, you can reduce the max number of tokens generated if you want a more concise response, or increase the number of retrieved chunks if the agent is missing useful context. These parameters give you fine-grained control over your agent’s behavior, ensuring that it meets your specific requirements.

 

Driving continuous improvement with specialization and evaluation

Specialization is tightly coupled with evaluation, a systematic process of assessing your agent’s performance across multiple dimensions. Assessments encompass both end-to-end benchmarks, such as overall response accuracy and groundedness, as well as granular component-level metrics like retriever precision. By analyzing performance at both levels, you can pinpoint specific areas for improvement in your AI system.

The relationship between specialization and evaluation creates a powerful feedback loop that drives continuous improvement. Evaluation helps identify specific weaknesses—whether in document retrieval, answer synthesis, or domain-specific handling—while specialization provides the mechanisms to address these gaps. Through methodical testing across varied queries and careful analysis of both successes and failures, teams can systematically enhance their agent’s performance over time.

 

Specializing RAG agents with Contextual AI

The Contextual AI Platform offers a comprehensive set of tools for AI teams to specialize RAG agents and evaluate the improvements realized.

To start, our tuning API lets you jointly tune multiple components of your RAG agent to significantly boost its performance on domain-specific tasks and workflows. Creating a fine-tuning job is extremely simple and requires only a few lines of Python code:

response = client.agents.tune.create(
    agent_id=agent_id,
    train_dataset_name=train_dataset,
    test_dataset_name=val_dataset
)

print("Tune job created. ID: {response.id}")

If you have your own fine-tuning dataset, that’s great. When preparing your dataset, we recommend having very dense data with up to 8,000 tokens per row, rather than long but terse data with thousands of rows containing only hundreds of tokens each. Additionally, if you know your agent is performing poorly on specific types of questions, be sure to include more of these examples in your fine-tuning set.

If you don’t have a dataset for fine-tuning, you can still unlock improved performance on your agent. Our fine-tuning API is capable of generating extensive synthetic data under the hood, making it extremely simple for you to tune your model for improved performance. To steer the synthetic pipeline and ensure that the data is sufficiently diverse, you can input a list of end-user personas (roles and responsibilities) that will inform the type of questions that are being generated.

The Contextual AI platform also offers you the ability to modify your system prompt and easily configure parameters for various components of your RAG agent. This includes adjusting the weighting between lexical and semantic search, the number of chunks retrieved and reranked, and the creativity and variety of generated responses.

response = client.agents.evaluate.create(
    agent_id=agent_id,
    metrics=["equivalence", "groundedness"],
    evalset_name=eval_dataset
)
print(f"Eval job created. ID: {response.id}")

Finally, you can run evaluations on the Contextual AI platform. You can create natural language unit tests (LMUnit) or use a language model to check if the response matches the gold answer (equivalence) and if the claims are supported by retrieved knowledge (groundedness). Both of these tasks only require a few lines of Python code. Here’s an example for the latter:

 

Get started today

Try out the Contextual AI Platform today! We have an exciting upcoming roadmap for specialization features, including fine-tuning with thumbs up and thumbs down feedback from end-users (aka alignment) and question-answer pairs. If you would like to try fine-tuning and evaluating your RAG agent on the Contextual AI Platform, check out our Tune & Evaluation Guide to get started.