When building agentic AI systems you need relevant, high-quality data to guide your agents behaviour.
It's important to restrict the language model to use only the relevant context to reduce hallucinations and provide accurate responses. The best way to do that would be to train the model with accurate training data, but that is not usually viable. Alternatively, a model can be finetuned with your own datasets. Third option is to use commercially available models, and to utilize RAG (Retrieval-Augmented Generation).
By using RAG, only the information that is relevant to the prompt is added to the model's context.
This external memory can consist of text documents, chat histories, memos, contracts, excel sheets, database tables and even videos, images, and audio. Just as memory is important for us humans, it is also so for AI.
How well your RAG system performs is dependent on the quality of the retriever, which has two main functions: indexing and querying data from the external memory. Indexing is all about processing the data so it can be quickly retrieved later.
In this post, we focus on the quality and accessibility of the data, so it can be easily indexed for later AI operations. It is important that data is sourced efficiently, ethically, and is protected adequately. It needs to be up-to-date, might need enriching, and be accurate. How to achieve this then?
Data Sourcing and Collection
The foundation of any effective RAG system begins with systematic data collection. Map out every place where valuable knowledge already lives inside the company: Confluence pages, Git repositories, ticket systems, call transcripts, shared drives, and data warehouses. Then identify carefully selected public sources that are either licensed or clearly permissive. For each source, document ownership, update cadence, and allowed use, so you can trace back any issues.
Data Processing and Enrichment
Raw files come in many shapes: PDFs, Markdown and SQL rows. Convert everything to a common format such as JSON with clearly named fields. Text documents need to be properly segmented into chunks that are easily digestible by the language model. The chunking strategy should consider document structure and the typical queries of your users. Good structure reduces noise and speeds up vector search.
Enrich your data with metadata tags, categories, and semantic annotations. You can even summarise long sections with a small model so agents can preview content before retrieving full text. This additional context helps the retriever understand not just what the content says, but what it's about and when it's relevant. Consider adding timestamps, topic classifications, and relationship mappings between different pieces of content.
Clean your data consistently. Remove duplicates and standardize formats. Inconsistent data leads to poor retrieval performance and confused AI responses.
Maintaining Data Freshness
Stale context leads to stale answers. Implement automated pipelines to regularly update your knowledge base. Set up monitoring for source changes and establish refresh schedules based on how often content changes. A nightly sweep may work for policy handbooks but product prices might need hourly updates. When data changes rapidly, sometimes it can be more effective to pull context directly from the ERP system.
Quality Assurance and Validation
Implement multi-layered validation processes. Use automated checks for formatting consistency, fact-checking against authoritative sources, and semantic coherence. Complement these with human review for nuanced content that requires domain expertise.
Create feedback loops from your AI system's performance back to your data quality processes. Monitor which pieces of content are frequently retrieved but lead to poor responses, indicating potential quality issues that need addressing.
Privacy and Security Considerations
Ensure your data collection and storage practices comply with relevant privacy regulations like GDPR or EU AI Act. Implement proper access controls all the way through the RAG stack, data encryption at rest, and secure storage practices. Perform automatic redaction of personal data and confidential documents that the agent should never see.
Measuring Success
Track metrics to assess your data pipeline performance: retrieval precision, user satisfaction with AI responses, the frequency of hallucinations or inaccurate answers, and the speed of information retrieval.
Regular audits of your data pipeline help identify bottlenecks, quality issues, and opportunities for improvement. The goal is to ensure every piece of data contributes positively to your AI system's performance and the language model sees precisely what it needs and nothing more.
Conclusion
High-quality data is the cornerstone of effective agentic AI systems. While the initial effort to establish proper data collection, processing, and maintenance practices requires significant investment, the payoff in terms of AI system reliability and user satisfaction is substantial.
Remember that data quality is not a one-time achievement but an ongoing process. As your domain evolves and your AI use cases expand, your data strategy must evolve alongside them. Start with a solid foundation of clean, well-sourced data, and build systematic processes to maintain and improve that quality over time.
The most sophisticated AI architecture cannot compensate for poor-quality data, but high-quality data can make even simpler systems remarkably effective. Invest in your data, and your agentic AI systems will reward you with accurate, reliable, and trustworthy performance.
What's Next?
In our next blog post, we'll dive deeper into the implementation details that transform high-quality data into an efficient retrieval system. We'll go through indexing strategies, chunking techniques for different content types, metadata models that enhance retrieval precision, and domain-specific functions that improve your RAG system's usefulness!
