Structuring Prompts for Accessing Knowledge Stores

Agents often require information that isn't present in their immediate context window or part of their initial training data. To perform effectively, especially in tasks requiring up-to-date or specialized knowledge, they must access external knowledge stores. These stores act as a form of persistent, long-term memory, ranging from structured databases and document repositories to specialized APIs. This section focuses on how to structure your prompts to enable an agent to successfully query these external knowledge sources, retrieve relevant information, and integrate it into its operational workflow.

Guiding Agents to Query Knowledge Stores

When an agent needs to tap into an external knowledge store, your prompt's primary role is to bridge the agent's internal reasoning with the mechanics of data retrieval. This involves clearly signaling when a query is necessary, what information is sought, and how to interact with the specific knowledge store.

Important considerations for your prompts include:

1. Intent Recognition: The agent must first understand that the current task or user query necessitates fetching external data. Your prompt can define conditions or triggers for this.
2. Query Formulation: The agent needs to translate its information need into a valid query format understandable by the knowledge store. This could be natural language, a structured query language (like SQL), or parameters for an API call.
3. Tool Selection (if applicable): If multiple knowledge stores or retrieval tools are available, the prompt should guide the agent in choosing the most appropriate one.
4. Result Processing: Once data is retrieved, the agent must be instructed on how to parse, interpret, and utilize this new information to continue its task or answer a query.

Prompting Techniques for Knowledge Store Interaction

Let's look at specific methods for structuring prompts to facilitate these interactions.

1. Explicit Instructions for Tool or Function Invocation

For agents designed with tool-using capabilities, accessing a knowledge store is often abstracted as calling a specific tool or function. Your prompt must clearly define how the agent should signal its intent to use such a tool and the format for its arguments.

Imagine an agent has access to a tool named search_product_database which takes a product_query (string) and filters (optional dictionary) as input.

A prompt might include instructions like: "If you need to find information about a product, use the search_product_database tool. You must provide the product_query. You can optionally provide filters such as {'category': 'electronics', 'in_stock': true}. To use the tool, output a JSON object in the following format:

{
  "tool_name": "search_product_database",
  "tool_input": {
    "product_query": "name of product or description",
    "filters": {
      "filter_key_1": "value1",
      "filter_key_2": "value2"
    }
  }
}

"

This approach requires you to define:

• Tool Description: What the tool does (e.g., "find information about a product").
• Invocation Syntax: How the agent should format its request to use the tool (e.g., the JSON structure).
• Parameter Specification: The names and expected data types or formats for each input (e.g., product_query as a string, filters as a dictionary).

2. Generating Natural Language Queries for Semantic Search

Many modern knowledge stores, particularly vector databases, are optimized for semantic search using natural language queries. Instead of rigid SQL, the agent can form a question or descriptive phrase. Your prompt should guide the agent to formulate effective natural language queries.

For instance, if an agent needs to query a company's internal documentation (stored in a vector database and accessed via a query_internal_docs tool):

Prompt snippet: "To find relevant information in the company's internal documents, use the query_internal_docs tool. Formulate a clear and specific question or a descriptive phrase that captures the core of what you're looking for. Example: If the user asks 'What's our policy on parental leave?', a good query for query_internal_docs would be 'parental leave policy details'. Tool usage:

{
  "tool_name": "query_internal_docs",
  "tool_input": {
    "natural_language_query": "your detailed question or search phrase"
  }
}

"

Tips for prompting natural language query generation:

• Encourage specificity in the query.
• Suggest including distinguishing terms or concepts.
• Provide examples of good queries (few-shot prompting) to illustrate desired query styles.

3. Guiding Structured Query Language (SQL) Generation

For relational databases, agents might need to generate SQL queries. This is a more demanding task for an LLM, as it requires understanding the database schema. Your prompt must provide sufficient information about the schema and guide the translation from natural language intent to SQL.

Providing Schema Information: You can include a simplified version of the relevant table schemas directly in the prompt.

Example: "You have access to a database with the following tables: products table:

Column Name	Data Type	Description
product_id	INTEGER	Unique ID for the product
name	TEXT	Name of the product
category	TEXT	Product category
price	REAL	Price of the product
stock_level	INTEGER	Current stock quantity

orders table:

Column Name	Data Type	Description
order_id	INTEGER	Unique ID for the order
product_id	INTEGER	ID of the product ordered
quantity	INTEGER	Quantity ordered
customer_id	INTEGER	ID of the customer

To query this database, generate a SQL query and provide it to the execute_sql_query tool. Example: If asked 'Find all laptops under $500', you might generate: SELECT name, price FROM products WHERE category = 'laptop' AND price < 500;

Tool usage:

{
  "tool_name": "execute_sql_query",
  "tool_input": {
    "sql_query": "YOUR_GENERATED_SQL_QUERY"
  }
}

"

Elements for SQL generation prompts:

• Concise Schema Description: Provide table names, column names, data types, and brief descriptions. Avoid overwhelming the LLM with unnecessary detail.
• Targeted Examples: Show few-shot examples of natural language questions and their corresponding SQL queries, relevant to the provided schema.
• SQL Dialect (if significant): If the database uses a specific SQL dialect with unique syntax, mention critical aspects or provide examples reflecting it.
• Safety: Instruct the agent to primarily generate read-only queries (SELECT statements) unless write operations are explicitly intended and managed by a secure tool interface designed for such modifications.

Interaction flow when an agent accesses an external knowledge store, guided by prompts at various stages from identifying the need to processing the results.

4. Handling and Integrating Query Results

Retrieving data is only part of the process. The agent then needs to make sense of it. Your prompts should guide this next stage:

• Parsing: If the data comes back in a structured format (e.g., JSON from an API, rows from a database), the agent might need instructions on how to parse it. This is often handled by the tool interface itself, but the agent needs to know what to expect in terms of data structure.
• Summarization or Extraction: For large amounts of text or data, prompt the agent to summarize the findings or extract only the specific pieces of information relevant to its current goal.
  • Example: "From the retrieved document, extract the main eligibility criteria for the loan product and list them."
• Answering Questions: If the query was initiated by a user question, instruct the agent to use the retrieved information to formulate a comprehensive and accurate answer.
• Updating Internal State: The retrieved information might need to be incorporated into the agent's short-term memory (context) or influence its ongoing plan.
• Error Handling and Fallbacks:
  • No Results: What should the agent do if a query returns no relevant information? Prompt it to inform the user, try rephrasing the query with different terms, or attempt an alternative strategy.
    • Example: "If the search_product_database tool returns no results, try broadening your product_query or removing some filters. If still unsuccessful, inform the user that the product could not be found based on the provided criteria."
  • Tool Errors: If the tool itself reports an error (e.g., invalid SQL syntax, API endpoint unavailable), prompt the agent on how to react. This might involve re-trying the query after a modification, reporting the error clearly, or asking for clarification if the original request was ambiguous.

Managing Schema Complexity for LLMs

When dealing with structured knowledge stores like SQL databases or complex APIs, providing the full schema or API documentation can consume a significant portion of the agent's limited context window. Here are strategies to manage this:

• Relevance Filtering: Only provide schema information for tables or API endpoints directly relevant to the kinds of tasks the agent is expected to perform. Prune away irrelevant details.
• Summarization: Describe schemas using concise natural language or simplified formats. For SQL, list table names with their most important columns and their data types, omitting less critical columns or constraints unless specifically needed for the task.

  # Example of a summarized schema for a prompt
  Available tables:
  - customers (customer_id INT, name TEXT, email TEXT, city TEXT)
  - orders (order_id INT, customer_id INT, order_date DATE, total_amount REAL)
  - products (product_id INT, name TEXT, category TEXT, price REAL)
  

• Multi-Turn Schema Exploration: For very complex schemas, you could design an interaction where the agent can first ask for a list of available tables or API categories, then request detailed schema information for specific ones it deems relevant. This iterative approach requires a more sophisticated interaction loop but can save valuable context space in initial prompts.
• Out-of-Band Knowledge: For very stable and frequently used schemas, some structural information might be implicitly learned by the LLM during fine-tuning (if applicable), reducing the need to pass all details in every prompt. However, for general-purpose agents interacting with diverse or evolving data sources, explicit schema provision in the prompt generally leads to more reliable behavior.

Security Considerations

While the focus here is on structuring prompts for access, it's important to remember that the actual connection to knowledge stores should be handled by secure tool interfaces. Prompts should not contain sensitive information like API keys, database credentials, or full connection strings. Instead, the prompt guides the agent to use a tool, and that tool is responsible for implementing secure authentication and authorization mechanisms. Your prompt can, however, instruct the agent to be mindful of data sensitivity when formulating queries or presenting results, for example: "Do not request or display Personally Identifiable Information (PII) like full addresses or payment details unless explicitly necessary for the task and you have confirmed authorization through the appropriate user verification step."

By carefully structuring your prompts, you empower AI agents to effectively tap into vast external knowledge stores. This transforms them from isolated processors into informed assistants capable of retrieving and utilizing a wide array of information to accomplish their goals, greatly expanding their utility for complex, real-world tasks that depend on access to dynamic or specialized data.