Intent-Based Content Search and Retrieval: Key Steps To Resolving Complex Queries

Consider a technical documentation retrieval system. When a user queries, "How do I create a new document?" or "How do I update a section?"

The system reduces the computational load by narrowing down the context before querying the LLM, leading to faster response times.

However, the critical question is how to handle a large number of queries simultaneously, making it suitable for applications with high user traffic.

Traditional keyword-based search methods often fail to understand user intent, producing suboptimal results. An advanced approach, combining Natural Language Processing (NLP) and large language models (LLMs), can significantly enhance content search and retrieval by focusing on the intent behind queries.

Understanding the approach

During our research and development phase, we applied several key steps based on the fundamentals of understanding the query and its primary intent. The key points involved:

1. Verb interpretation
2. Subject identification
3. Keyword extraction
4. Subject-Verb agreement
5. Context generation
6. Database search
7. Prompt creation
8. LLM response

When to use this approach

This approach is essential and particularly beneficial for content-critical businesses in legal,?medical, or?regulatory compliance areas. In such companies, the user may:

• Runs complex queries.
• Search for high-precision content.
• Create dynamic content.

Implementing an intent-based content search and retrieval system using NLP and LLMs can provide more precise and contextually relevant responses, improving user experience and operational efficiency.