Background
User queries may involve simple information searches, tool invocations, casual chatting, and more. How can we perform intent analysis (or query classification) on user queries in the context of a specific AIAgent system?
Scenario Overview of a Specific AIAgent
The dataset of the RAG system in the specific AIAgent is as follows: (It is evident that this is a chunking of a resume)
resume_chunks = [
basic_chunk,
skills_chunk,
introduction_chunk,
certification_chunk,
project_chunk_1,
project_chunk_2,
project_chunk_3,
......
]
Intent Analysis Using the Dataset of a Specific AIAgent
Method 1: Keyword Extraction and Matching
In the following article, I have explained how to split datasets into specific chunks using a keyword density algorithm. https://strictfrog.com/en/2026-01-31-structural-chunks-design-of-rag/
type_keywords = {
"basic_chunk" = ["name", "first name", "age", "years old", "birthdate", "university", "college", "person"...],
"skills_chunk" = ["python", "C++", "iOS", "Android", "Java", "PHP"...],
"introduction_chunk" = ["eager to learn", "stress-resistant", "efficient", "agile development",...],
"certification_chunk" = ["Computer Level 2", "CET-4", "CET-6", "IELTS", "AWS",...],
"project_chunk" = ["project experience", "role", "duration",...]
}
Example:
UserQuery: I want to find a person named Louis with 5 years of Python development experience.
By iterating through type_keywords in Python, keywords like "name", "person", and "Python" were found in the User Query.
Intent analysis result:
The user intends to search for "basic_chunk" and "skills_chunk" in the specific AIAgent.
In plain terms: the user wants to look up personal information and skills of someone.
Method 2: Intent Analysis Using LLM Prompting
Assume the architecture of the specific AIAgent is as follows:
Chat (casual chat)
|
FAQ (dataset lookup)
|-- basic
|-- skills
|-- introduction
|-- certification
|-- project
|-- other
Tools (tool invocation to execute tasks)
|- sendMails
|- getCurrentWeather
|- ......
The architecture diagram is as follows: (Intent analysis only uses the first layer: Intent Router)
For the complete architecture, you can refer to this article: https://strictfrog.com/2026/02/19/AIAgent%E4%B9%8B%E5%AE%8C%E6%95%B4%E7%9A%84%E7%B3%BB%E7%BB%9F%E7%B3%BB%E6%9E%B6%E6%9E%84/
User Query
↓
1️⃣ Intent Router
↓
┌────────────┬────────────────────────────┬──────────────┐
│ Chat │ FAQ │ Tool_Required│
│ (LLM only) │ (basic|skills|...|project) | (ReAct loop) │
└────────────┴────────────────────────────┴──────────────┴
↓
......
Router Prompt:
From Method 1, the initial analysis result is:
first_result = “The user wants to search for ‘basic_chunk’ and ‘skills_chunk’ in the specific AIAgent.”
functionCalling.json is the structured JSON file describing all tools.
prompt = ‘
You are an intent analyst specialized in deep analysis of user queries.
The preliminary analysis result is: {first_result}.
The dataset features are known as: {The resume dataset is divided into the following categories: basic (personal info including name, age, birthdate, education, etc.), skills (skills such as Python, Java and other IT programming languages), introduction (self-introduction), certification (certificates including IT skill certifications and TOEFL, etc.), project (project experience)}.
Known available Tools: {functionCalling.json}.
User query: {query}.
Analyze the "user query" into one or more of the following categories:
1. Chat
2. FAQ: basic
3. FAQ: skills
4. FAQ: introduction
5. FAQ: certification
6. FAQ: project
7. tool_required
Return JSON:
{
"type": "..."
......
}
’
Review of the “Router Prompt”
Integrating the specific AIAgent dataset and toolset:
1. Use keyword matching based on RAG’s chunking characteristics for preliminary analysis.
2. Pass dataset and tool features into the prompt to enable the LLM to perform more precise deep analysis.
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