AWS Public Sector Blog
From keywords to conversations: Reimagining document discovery with Amazon Bedrock
Organizations that host vast repositories of reports, strategies, evaluations, and other content often face challenges in helping users efficiently navigate and access this wealth of information. This blog post explores how cutting-edge artificial intelligence (AI) techniques, powered by Amazon Web Services (AWS), can transform how users interact with knowledge bases. Specifically, we propose integrating a large language model (LLM) and Retrieval Augmented Generation (RAG) framework to revolutionize search and discovery. This post also explains the limitations of traditional search, the potential of combining LLM and RAG, and our vision for a next-generation search platform.
At AWS, we believe the future of knowledge discovery isn’t about queries—it’s about conversations. By combining LLMs and RAG on Amazon Bedrock, organizations can transform static document troves into dynamic, intuitive interfaces for discovery.
Limitations of traditional search engines
While keyword-based search has been a reliable tool, it has inherent limitations. Users must rely on specific phrases and terminology to find relevant documents, which becomes challenging when searching for complex information requiring deeper language understanding. Traditional search engines primarily match terms without considering context or meaning, often leading to information overload or difficulty in locating highly relevant materials.
For organizations with expansive knowledge bases, these challenges are amplified. Despite efforts to categorize resources, users often struggle to find answers to real-world questions buried within the content.
The breakthrough: LLM + RAG = Context-aware discovery
Large language models (LLMs) are AI algorithms trained on massive volumes of text data, enabling them to generate human-like language and reasoning capabilities. When queried, LLMs provide contextual responses rather than simply matching keywords.
Retrieval Augmented Generation (RAG) enhances this capability by combining LLMs with powerful information retrieval techniques. RAG systems first search across available knowledge sources to gather relevant passages, facts, or documents. The LLM then integrates this external information to generate high-quality, contextually relevant responses.
A global health nonprofit implemented this approach and saw:
- 60% faster discovery of program evaluation data
- 40% reduction in “no results found” queries
- First-ever cross-report insights (e.g., linking education outcomes to nutrition initiatives)
Revolutionary potential for document search
By combining LLM and RAG algorithms with indexed content, organizations can enable truly intelligent search experiences. For example, instead of searching for “nutrition programs Africa,” users could ask, “How is childhood nutrition being improved in sub-Saharan Africa?” The system would automatically identify and serve the most relevant reports, evaluations, and excerpts.
Additional benefits:
- Conversational Q&A: Users can query resources using natural language questions instead of relying on keywords.
- Connecting insights across data silos: LLMs can synthesize information from multiple reports and databases, delivering integrated perspectives.
- Multidimensional relevance matching: Relevance is determined by semantics, intent, and specificity, not just keyword matching.
- Personalized recommendations: Usage patterns inform unique user interests, enabling tailored suggestions and responses.
- Impact tracking: LLMs can rapidly analyze documents to extract key statistics, outcomes, and impact, providing a quick grasp of program effectiveness.
Our vision for next-generation search
We envision a next-generation search platform as the go-to resource for easily retrieving organizational insights. This system combines a user-friendly conversational interface with AI-powered relevance matching, recommendations, and analytics.
Both casual visitors and professionals would benefit from seamless access to institutional knowledge.
Democratizing access to the latest learnings and evaluation outcomes can amplify impact across communities. Additionally, connecting insights across disparate databases and reports can uncover unseen relationships, enabling the discovery of new solutions to complex challenges. Organizations have a unique opportunity to spearhead innovation in public sector search functionalities powered by AI.
“The best ideas in your organization shouldn’t be the hardest to find.” Let’s build a search experience worthy of your mission.
Conclusion
Integrating LLMs with the RAG framework can transform static information repositories into intelligent assistants for naturalistic search and discovery. Modernizing search platforms with these technologies promises immense value, enabling users worldwide to access extensive knowledge assets with ease. This undertaking also opens exciting research frontiers for responsibly applying AI in public sector search applications.