LLM Integration Patterns

Best practices for integrating the Abba Baba API with Large Language Models like GPT.

The Abba Baba API is designed to be a powerful tool for AI agents built on Large Language Models (LLMs). By connecting your LLM to our structured e-commerce data, you can create sophisticated shopping agents that can understand user needs and recommend real-world products. This guide outlines common patterns for a successful integration.

Pattern 1: The "Tool-Using" Agent

This is the most common and effective pattern. The LLM acts as a reasoning engine that can decide *when* to use external tools. You define the Abba Baba Search API as a "tool" that the LLM can call.

1. User Prompt: The user gives the LLM a shopping-related prompt, e.g., "Find me a good gift for my dad who likes coffee."
2. Tool Selection: The LLM analyzes the prompt and determines that it needs to find products. It decides to call the `abba_baba_search` tool.
3. Query Generation: The LLM generates a relevant search query for the tool, such as `"gifts for coffee lovers"`.
4. API Call: Your backend code executes the call to the Abba Baba API with the generated query.
5. Response to LLM: The JSON results from our API are returned to the LLM as context.
6. Final Answer: The LLM synthesizes the search results into a natural language response for the user, e.g., "I found a few great options: an AeroPress Coffee Maker, a subscription to a coffee bean service, and a high-end electric kettle."

Pseudo-code for a Tool-Using Agent

function abba_baba_search(query: string, filters: object): list_of_products {
  // Your backend code to call the Abba Baba API
  // 1. Make a POST request to /v1/search with the query and filters
  // 2. Return the 'data' array from the JSON response
  return call_abba_baba_api(query, filters);
}

// --- Agent Execution ---

user_prompt = "Find me some noise-cancelling headphones under $300 for commuting"

// 1. LLM determines the intent and extracts parameters
search_query = "noise-cancelling headphones for commuting"
search_filters = { "max_price": 300 }

// 2. Your code calls the defined tool
api_results = abba_baba_search(search_query, search_filters)

// 3. LLM synthesizes the results into a final answer
final_prompt = f"""
  A user asked: {user_prompt}
  I found these products: {api_results}
  Please present these options to the user in a helpful, natural way.
"""
final_answer = call_llm(final_prompt)
print(final_answer)

Pattern 2: Prompt Stuffing (Less Effective)

Another, simpler pattern is to pre-fetch a list of potentially relevant products and "stuff" them into the LLM's prompt as context. This can work for very narrow use cases but is generally less flexible and scalable.

• Pros: Simpler to implement; no complex agent logic required.
• Cons: Not interactive, limited by context window size, may provide irrelevant information to the LLM, and consumes more tokens.

Best Practices for LLM Integration

• Let the LLM Generate the Query: Train your LLM to identify user intent and generate a descriptive search query. An LLM is better at this than simple keyword extraction.
• Summarize, Don't Overwhelm: Return only the most important product fields (`title`, `price`, key attributes) to the LLM. Avoid sending the full, verbose API response, as it can fill up the context window.
• Use Filters for Precision: Instruct your LLM to extract specific constraints from the user's prompt (like price, brand, or color) and use them in the `filters` object.
• Handle "No Results": Instruct your LLM on how to respond gracefully if the API returns no products, e.g., "I couldn't find any products matching that specific criteria. Could we try broadening the search?"

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