If you’re building an AI application that needs to retrieve and reason over information, search is a core part of how your system thinks. And the quality of your queries directly impacts the relevance of search results. Relying solely on black-box prompt rewrites won’t provide enough control. To ensure the data is relevant and aligned with your system’s goals, you need to use clear query-shaping logic.
This article demonstrates how to design and optimize search workflows for AI apps, such as AI agents and business copilots. We’ll cover the basics of why query optimization is important, the techniques for optimizing search queries, the top search tools, how to use them and best practices for data retrieval.
Why is query optimization important?
In AI-native applications, the system often needs to look something up to fill in missing details, validate facts or complete a task. That could mean pulling data from the web, a database or an API. Query optimization means helping the system ask more efficient questions when it needs to perform such a search operation.
Tools like ChatGPT, GitHub Copilot and other AI agents also perform this kind of optimization behind the scenes. For example, if you ask ChatGPT something like “What’s the latest on global climate policy?” it doesn’t send that raw prompt to a search API. It rewrites the query to focus on recent events, adds a time filter, picks the right source like news articles or government sites and then pulls the most relevant info. If you’re building your own AI-native application and want it to return useful results, you’ll need to do the same.
Top query optimization strategies
Depending on your system’s needs, you can apply a few query optimization techniques, as shown below.
Machine learning/LLM-based query optimization
This approach uses natural language techniques to reshape search queries before they’re sent to a search engine or retrieval system. It handles tasks like rewriting vague input, detecting intent, filling in structured query templates or adding useful filters. This can be done with classic natural language processing (NLP) tools, small machine learning (ML) models or lightweight transformers.
For more advanced cases, you can use hosted language model APIs like ChatGPT, Claude or Gemini to handle dynamic prompt engineering. For example, if a user gives your AI application a goal that involves searching the internet for information, you can send that input to the language model to rewrite or refine the query before running the search.
As shown in the image above, this workflow takes plain user input and gradually transforms it into a more useful search query by applying lightweight language processing steps.
It’s worth mentioning that using hosted LLMs comes with tradeoffs such as added latency and API costs. Also, unless you apply strong constraints, LLMs may introduce hallucinated filters or assumptions that can hurt search accuracy (like inventing a preferred data source or applying a category that wasn’t asked for). However, they can still be more effective than building and maintaining a custom NLP model from scratch. Weigh your options carefully and, if you go with an LLM, use guardrails or templated prompting to reduce risk.
Later, we’ll explore practical examples of how the machine learning/LLM-based query optimization technique fits into real search tools.
Rule-based query optimization
Unlike the ML/LLM-based query optimization technique, this method uses predefined rules to improve or reshape search queries before they’re run. These rules might expand a query with synonyms, block noisy keywords, insert filters like date or category or fill out a template based on known patterns. It’s a faster and more reliable way to improve search quality without needing any models or training data.
As shown above, the system doesn’t try to understand the query. Instead, it applies known logic to adjust it. Let’s say a user types “find jobs for devs.” A rule-based system might do the following before sending the query to the search engine:
- Expand “devs” to include “developers” and “software engineers”
- Add a filter for job-related content
- Inject a default location filter if the user’s profile includes a country
And the final query could look like:
| (“devs” OR “developers” OR “software engineers”) AND type:job AND location:”Nigeria” |
This kind of optimization just needs a clear set of rules based on the domain and user context. They work well in domains with fixed terms, filters or content types and are easy to control without unexpected behavior.
Behavior-based optimization
Search systems can become smarter by learning from how people interact with results. When users click on certain links, ignore others or frequently rephrase the same query, that behavior becomes a signal. Over time, the system can use those signals to suggest better phrasing before the query runs.
In this technique, the system learns by observing which inputs lead to useful results, without needing to understand the language itself. If users searching “freelance tax advice” tend to click on results about “self-employment deductions,” the system can prioritize or suggest those terms in future searches.
Most behavior-based systems rely on logs, click-through rates and rerun query patterns. These signals can be modeled using simple heuristics or used as feedback to improve query rewriting, ranking or retrieval. Over time, this feedback loop helps the system learn what good results look like. It can spot patterns, surface better queries and correct inputs that tend to lead to poor results.
Query optimization is only part of the job. After deciding on a technique, you’ll also need the right tools to run those queries and retrieve useful data.
Top search tools and how to use them
Here’s a breakdown of the top search tools and how to use them.
Web search APIs
Web search API tools allow systems to search the internet like a person would, but in a more automated and structured way. A few examples are Google Custom Search, Bing Search API and SerpAPI. Whichever API you choose, you can pair it with ML/LLM-based query optimization to get more relevant results.
Say a user comes to your AI application with a goal like:
| “Plan a 3-day trip to Kyoto. I want hidden gems, not the usual tourist spots.” |
Suppose you send that query raw to Google or any search API. You’ll likely get generic “Top 10 things to do in Kyoto” results or nothing useful at all. However, with ML/LLM techniques we discussed earlier, you can reshape the user’s goal into something the search engine can work with.
Here’s a simple example using OpenAI and SerpAPI:
| import openai import os from serpapi import GoogleSearch # Use OpenAI to rewrite the search query user_goal = “Plan a 3-day trip to Kyoto. I want hidden gems, not the usual tourist spots.” openai.api_key = os.getenv(“OPENAI_API_KEY”) prompt = f”Rewrite the following travel goal as a focused search query: {user_goal}” response = openai.ChatCompletion.create( model=”gpt-4″, messages=[{“role”: “user”, “content”: prompt}], temperature=0.4 ) refined_query = response[‘choices’][0][‘message’][‘content’] print(“Refined Query:”, refined_query) # Use SerpAPI to run the search search = GoogleSearch({ “q”: refined_query, “api_key”: os.getenv(“SERPAPI_KEY”), “num”: 5 }) results = search.get_dict() print(“Search Results:”, results.get(“organic_results”, [])) |
In this example, the script takes a user goal, rewrites it into a sharper search query using OpenAI API and then uses SerpAPI to run the actual web search. We also set the temperature to 0.4 to balance consistency and flexibility so that the model can generate structured yet slightly rephrased queries.
Running the code is more likely to return blogs, forums and niche recommendations instead of mainstream travel articles, which is exactly what the user asked for.
AI-native prompt search APIs
We also have newer AI-powered search APIs like Tavily, Jina.ai and Perplexity API. They are built to handle prompts and open-ended questions directly and don’t require strict keyword-based queries. For example, if a user came to your AI application with the query:
| “What’s happening with global climate policy this week?” |
You could easily send that to the Perplexity API to fetch up-to-date information, as shown below.
| import requests response = requests.post( ‘https://api.perplexity.ai/chat/completions’, headers={ ‘Authorization’: ‘Bearer YOUR_API_KEY’, ‘Content-Type’: ‘application/json’ }, json={ ‘model’: ‘sonar-pro’, ‘messages’: [ { ‘role’: ‘user’, ‘content’: “What’s happening with global climate policy this week?” } ] } ) print(response.json()) |
This will return a fresh, AI-generated response based on the most current data available. Instead of rewriting queries or extracting keywords manually, you can send the full prompt as-is and get context-aware results. Also, responses from AI-native search APIs like Perplexity are typically generated summaries, not raw source links. If your application requires source-grounded retrieval, you should rely on their citations or use a traditional search API that provides direct links to source pages.
Vector search engines
Vector search engines are also powerful tools for building AI-native applications. These include tools like Pinecone, Weaviate, Qdrant and ChromaDB. They let you search your own unstructured data using embeddings. You embed your content, store it in a vector index and embed user queries at runtime to find the most relevant matches based on meaning rather than keywords.
You can combine vector search with the rule-based query optimization technique to make queries more precise before they’re embedded and searched.
Take this user query, for example:
| “Find remote jobs for devs” |
Instead of running this raw, you can use the rule-based query optimization technique to first:
- Expand “devs” to include related roles like “developers” and “software engineers”
- Add a filter for remote-only listings
- Inject a default location if the user has one on file
So that the final query might look like:
| (“devs” OR “developers” OR “software engineers”) AND type:job AND remote:true AND location:USA |
Here’s how you could apply this kind of logic before running a vector search with ChromaDB and SentenceTransformers:
| from chromadb import Client from chromadb.config import Settings from sentence_transformers import SentenceTransformer # Setup client = Client(Settings(chroma_db_impl=”duckdb+parquet”, persist_directory=”./chroma_store”)) collection = client.get_or_create_collection(“job_posts”) embedder = SentenceTransformer(“all-MiniLM-L6-v2”) # Rule-based query expander def expand_query(query, user_context=None): synonyms = [“developers”, “software engineers”] filters = [“type:job”, “remote:true”] location = f’location:{user_context[“location”]}’ if user_context and “location” in user_context else “” base_term = query.replace(“devs”, f’devs OR {” OR “.join(synonyms)}’) full_query = f'({base_term}) AND {” AND “.join(filters)}’ if location: full_query += f’ AND {location}’ return full_query # Process query user_input = “Find remote jobs for devs” user_context = {“location”: “USA”} expanded_query = expand_query(user_input, user_context) embedding = embedder.encode([expanded_query]) results = collection.query(query_embeddings=embedding, n_results=5) print(“Results:”, results) |
The code expands the user’s query using predefined rules, applies relevant filters and embeds the result for vector search with ChromaDB. This helps capture intent more clearly and improves the relevance of the retrieved results. Applying the rule-based optimization technique in vector searches gives you full control over how queries are shaped before search.
If a search fails to return relevant results, consider retrying the query with relaxed filters or expanded synonyms to broaden the search space. This can help capture additional relevant results when the initial query conditions are too strict or too narrow.
Also, keep in mind that the quality of embeddings and the alignment between the index and the query play a significant role in search performance. If your embeddings are not well-aligned or do not capture the query accurately, the results may be less relevant.
Best practices for getting optimal search results
To get better results from your search system, focus on how you prepare queries and structure your data.
Pre-query prep: Clean input, normalize spelling, structure intent
- Clean and normalize user input to remove noise and improve consistency.
- Break content into focused chunks to improve result relevance.
- Pick an embedding model that fits your domain and use it consistently.
Query shaping: Use rules, filters, prompt reformulations
- Use rules or lightweight models to rewrite vague or unclear queries.
- Add filters like tags or categories to narrow the search space.
- Avoid sending raw prompts when a cleaner query would work better.
Post-query evaluation: Monitor low-yield queries and adjust embeddings
- Track poor queries and refine your optimization logic over time.
- Adjust parameters like result count and index size to keep things fast and efficient.
These practices help your system return results that are more aligned with what users actually want.
Final thoughts
Search is at the core of how AI-native applications gather knowledge, make decisions and respond to users. But raw input alone isn’t enough. You need to shape and guide those queries to get meaningful results.
Evaluate your current search system to identify areas for improvement. Experiment with LLM-based query optimization, rule-based logic and vector search to refine results. Continuously monitor performance, track user feedback and adapt your approach as needed. You can also explore advanced tools like RAG scoring and LLMs to improve search relevance and keep your system aligned with user needs.