Transforming Customer Queries into Conversions with LLM-Powered Search

Introduction

When nearly 70% of visitors go straight to your search bar, you can’t afford for it to fall short. Yet most on-site search tools still rely on outdated keyword matching – returning irrelevant results or, worse, none at all. That’s why 80% of users abandon a site when the search doesn’t deliver.

Meanwhile, companies using smarter search are seeing real gains. Amazon’s conversion rate jumps from 2% to 12% when users use search. The reason: newer AI tools powered by large language models (LLMs) understand what people mean, not just what they type.

This article breaks down how LLM-powered search works, where it’s driving results in the real world, and how business leaders can start using it to improve customer experience and revenue without rebuilding their entire tech stack.

What Is LLM-Powered Search? (From Keywords to Understanding)

Most search tools work by matching exact words in a query to words in product names or content. If the words line up, the results show up. But users don’t always search that way. They type questions, describe problems, or use everyday language.

For example, someone might search for “shoes for bad knees.” A traditional search engine could miss the right results if those shoes are labeled as “orthopedic sneakers” or “joint support shoes.” It doesn’t recognize that those mean the same thing.

LLM-powered search works differently. It focuses on what the person is trying to find, not just the words they typed. It can understand intent, even if the phrasing is informal or uncommon. This leads to more useful results, and fewer dead ends.

How LLMs Enhance Search

Large language models (LLMs) make search more intelligent by understanding the meaning behind what people type, not just the individual words. They can process full sentences, recognize context, and interpret what the user is really asking for.

Instead of relying on a few keywords, LLMs can handle:

• Conversational queries, like: “I need a gift for someone who just started cooking.”
• Vague or indirect requests, such as: “clothes for unpredictable weather” or “laptop good for travel.”
• Unusual phrasing, where traditional search might fail due to lack of exact matches.

Because these models are trained on billions of text examples, they learn how people naturally express questions, needs, and preferences. This allows them to make smart connections, even when users aren’t specific.

Vector Search Alone vs LLM-Augmented Search

Vector-based search improves on basic keyword matching by retrieving results based on semantic similarity rather than exact terms. However, on its own, it still has limitations, especially when queries are vague, conversational, or require reasoning beyond simple similarity. LLM-powered search builds on vector retrieval by adding language understanding and generation capabilities, allowing systems to interpret intent, maintain context, and synthesize results. Here’s how the two approaches compare:

• Understanding complex or conversational queries
  Vector-based search retrieves results based on semantic similarity but does not interpret intent beyond that. LLMs can interpret full sentences and infer user intent.
  → Example: A query like “I need a gift for someone who loves quiet hobbies” may retrieve loosely related items via vector similarity, while an LLM can infer suitable categories such as puzzles, books, or drawing kits, even if those terms aren’t explicitly mentioned.

• Flexibility with data quality and format
  Vector search can retrieve relevant results from unstructured text but depends on consistent embeddings and content quality. LLMs can interpret and synthesize information from noisy or informal sources such as user reviews, support tickets, or loosely written product descriptions.

• Context handling and follow-up
  Vector-based search treats each query as a separate request unless additional session logic is implemented. LLMs can retain conversational context, enabling multi-step queries and natural follow-ups.

• Response quality and format
  Vector-based search returns ranked documents or items. LLM-augmented systems can summarize or generate direct answers using retrieved content (via retrieval-augmented generation), which is especially useful for support, documentation, and FAQs.

• Implementation effort
  Vector search focuses on embedding and retrieval pipelines. LLM-augmented search adds generation and orchestration layers, with additional trade-offs in cost and latency.

Hybrid Search Strategy: Combining Keyword and Semantic Approaches

Many companies exploring LLM-powered search still rely on keyword-based systems, especially when those systems are tied to structured filters, product IDs, or compliance rules. While semantic search handles natural language and vague queries well, it can miss specifics like SKUs or required specs.

A hybrid approach combines both methods: semantic understanding and precise keyword logic to get the best of both worlds. It’s especially useful for teams rolling out AI search gradually, supporting both broad and narrow queries (like “casual weekend jacket” vs “Uniqlo BlockTech parka”), and preserving business-critical filters while improving search relevance and user experience.

How It Works:

• Step 1: Semantic search finds matches by meaning. A tool like Pinecone or Weaviate looks at the overall meaning of the user’s query, so a phrase like “jacket for rainy hikes” might return results even if the product titles don’t use those exact words.

• Step 2: Keyword filters narrow the results. Tools like Elasticsearch apply rules to make sure important details are included, such as brand names, exact product IDs, or required features like “waterproof” or “zip pockets.”

• Step 3: Reranking chooses the best order. A model like Cohere Rerank or a GPT-based system scores and reorders the list based on both meaning and specific filters, so the most relevant and qualified items show up first.

Business Benefits + Use cases

LLM-powered search delivers clear, measurable benefits across customer experience, sales, and operations. From lifting conversions to cutting support costs, companies across industries are already seeing returns. Here are some of the most common ways it creates value across teams:

• Higher Conversion Rates
  LLM search improves product relevance by understanding user intent, even from vague or long queries. This leads to more users finding what they need and buying it.

• Fewer “No Results” Pages
  By recognizing synonyms, correcting typos, and inferring meaning, LLMs dramatically reduce dead ends in search, keeping users engaged instead of bouncing.

• Better Customer Experience
  Conversational search makes interactions more natural, while AI-powered support tools provide faster, more accurate answers.

• Increased Personalization and Engagement
  Search results and recommendations can be adapted in real time based on context, preferences, or user history, driving longer sessions and higher order values.

• Multi-Language Support
  A single model can understand and respond across dozens of languages, enabling consistent global service without maintaining separate search systems.

• Operational Efficiency
  LLMs reduce the load on support teams by deflecting tickets and speeding up internal knowledge access helping companies scale without adding headcount.

Use Cases and Success Stories

LLM-powered search helps people find what they’re looking for more easily when shopping or looking for service online. Instead of typing exact keywords, customers can use everyday language and still get useful, relevant results. Many companies are already using this to improve product discovery and increase sales.

E-Commerce

Amazon
Amazon uses generative AI to make product listings more relevant by rewriting titles and descriptions to better match a shopper’s search intent. For example, the AI may highlight “gluten-free” in a product result if that’s likely to matter to the customer. On the seller side, more than 100,000 sellers have used the tool to generate listings, with 80% of AI-generated content accepted with few or no edits.

Shopify
Shopify teamed up with OpenAI to make it easier for people to shop through ChatGPT. Users can install the Shopify app inside ChatGPT and ask for products in everyday language, like “show me eco-friendly running shoes”, and get results from Shopify stores, including links to buy.

Customer Support

Klarna launched an AI assistant powered by OpenAI that now handles two-thirds of all customer service chats across 23 markets and 35+ languages. In its first month, it managed  2.3 million conversations, equivalent to the workload of 700 full-time agents. It resolves common questions faster than humans, with fewer repeat contacts and high customer satisfaction.

Travel & Hospitality

Expedia Group integrated a ChatGPT-powered assistant into its iOS app to help travelers plan trips using everyday language. Instead of relying on filters, users can ask open-ended questions and get personalized results, backed by AI that processes 1.26 quadrillion variables like hotel type, dates, and price.

Core Technologies and Providers

Key technologies involved

LLM-powered search isn’t a single model – it’s a pipeline of components that turn questions into relevant and ranked answers or results. Here’s how it works in practice:

Embeddings: Encoding Meaning from Queries and Content

When a user types a query like “shoes that don’t hurt after long shifts on my feet”, the system doesn’t just look for exact matches. Instead, it uses a model like OpenAI’s text-embedding-ada-002 to convert the entire sentence into a dense vector – a list of numbers that captures the semantic meaning of the query.

At the same time, all product descriptions, help articles, or support content have already been embedded using the same method. This allows for semantic comparison, matching queries and content based on what they mean, not what they literally say.

Common tools:

• OpenAI (text-embedding-ada-002) – fast, high-performing model for capturing sentence meaning, used widely in production.
• Cohere Embed – multilingual embedding models that handle over 100 languages, useful for global applications.
• Hugging Face Transformers – open-source models like BERT or MiniLM for developers wanting full control over local or custom setups.

Vector Databases: Fast Retrieval at Scale

Once the query is embedded, it’s compared against millions of other embeddings stored in a vector database like Pinecone, Weaviate, or Elastic’s vector store. These databases quickly return the top N matches – items with the closest semantic meaning.

For example, in an e-commerce app, a vague query like “gift for someone who likes being outside” might return hiking gear, portable coffee kits, or weatherproof jackets, even if none of those terms were in the query, because the embeddings are close in vector space.

Popular tools for this step include:

• Pinecone – a fully managed vector database optimized for real-time semantic search.
• Weaviate – an open-source vector database with built-in machine learning modules.
• Elasticsearch – a widely used search engine that now supports hybrid search with vector fields alongside traditional keyword indexing.

Retrieval-Augmented Generation (RAG): Generating Answers from Trusted Content

In a support use case, it’s not always enough to link to a page. That’s where RAG comes in. It works like this:

1. Retrieve the top 3–5 most relevant documents using the vector search.
2. Feed those documents into a large language model (e.g., GPT-4) with a prompt like:“Based on the information below, answer the following customer question: [insert query].”
3. The model then generates a complete answer grounded in retrieved content, reducing hallucinations and increasing accuracy.

This approach powers AI chatbots, customer portals, and knowledge search tools that can give direct answers instead of just links.

Common tools for implementing RAG:

• OpenAI (GPT-4) – generates fluent, accurate answers based on provided context.
• LangChain – orchestration framework to connect retrieval systems with LLMs.
• LlamaIndex – indexing and retrieval layer designed specifically for RAG pipelines, works well with local or hosted models.

Reranking Models: Fine-Tuning What’s Shown First

Once you’ve retrieved relevant content, you often need to decide which result should appear first. A reranking model (like Cohere Rerank) scores each item based on how well it matches the original query and reorders the list accordingly.

For example, if the user types “wireless headphones for workouts”, and several items mention “wireless” and “headphones,” the reranker can prioritize the ones that also include “sweatproof” or “gym” attributes, even if they weren’t the top matches from the vector search.

Common tools for reranking:

• Cohere Rerank – fast, language-agnostic reranker that scores and sorts results by relevance.
• OpenAI (GPT-based reranking) – customizable reranking using prompt-based relevance scoring.
• Elastic's Learning to Rank plugin – traditional ML-based reranking integrated into search pipelines.

Conclusion

LLM-powered search goes beyond matching keywords. It helps systems understand what users are looking for and deliver more useful results, including direct answers when needed.

For customer-focused products, this is quickly becoming a standard requirement. As content and product catalogs grow, traditional keyword or basic semantic search often struggles with vague queries and follow-up questions. LLM-augmented search improves these experiences without forcing teams to replace their existing search systems.
Interested in applying LLM-powered search to your product? Book a free consultation to discuss your use case and technical constraints.