Most enterprise AI projects fail not because of weak models, but because of flawed architecture. Organizations are rushing to deploy Large Language Model (LLM)-powered search solutions, but a fundamental misunderstanding of how users actually interact with these systems is driving up costs and frustrating customers. A recent study from Coveo found that 72% of enterprise search queries fail on the first attempt, and Gartner predicts that many deployments won’t meet expectations. The core issue? A reliance on Retrieval-Augmented Generation (RAG) without first understanding what the user wants.
The Problem with Standard RAG: Intent Ignored
The standard RAG approach—embedding a query, retrieving similar content, then passing it to an LLM—works in demos, but falls apart in real-world applications. This is due to three key failures: the intent gap, context flood, and freshness blindspot.
Standard RAG treats intent as if it’s the same as context, but it isn’t. For example, a user typing “cancel” could mean canceling a service, an order, or an appointment. Without discerning this, systems often return irrelevant documents, leading to frustration.
Enterprises are awash in data: product catalogs, support articles, policies, and more. RAG models retrieve from all sources indiscriminately, burying useful information under noise. If a customer asks how to activate a new phone, they don’t need billing FAQs.
Finally, vector embeddings are time-blind. A last quarter’s promotion looks identical to this quarter’s, but presenting outdated offers erodes trust.
Intent-First: Classify Before Retrieving
The solution is a new architectural pattern: Intent-First. Instead of retrieving then routing, classify before retrieving. This means using a lightweight language model to parse the query for intent and context, then dispatching it to the most relevant sources.
This is not about better models; it’s about better architecture. Intent-First architectures use a lightweight language model to parse a query for intent and context, before dispatching to the most relevant content sources (documents, APIs, agents).
How It Works: A Step-by-Step Breakdown
An Intent-First system operates through a two-stage process:
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Intent Classification Service:
- Normalizes and expands the query.
- Predicts the primary intent using a transformer model.
- Extracts sub-intent based on the primary one (e.g.,
ORDER_STATUS,DEVICE_ISSUE). - Determines target sources based on intent mapping.
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Context-Aware Retrieval Service:
- Retrieves from filtered sources, excluding irrelevant ones.
- Personalizes results if the user is authenticated.
- Scores documents based on relevance, recency, personalization, and intent match.
Critical Safeguards: Healthcare as an Example
In industries like healthcare, additional safeguards are crucial. Intent categories must include clinical, coverage, scheduling, billing, and account-related queries. Clinical questions must include disclaimers and never replace professional medical advice. Complex queries should always route to human support.
Handling Edge Cases: Frustration Detection
The system must handle edge cases by detecting frustration. Keywords like “terrible,” “hate,” or “doesn’t work” should trigger immediate escalation to human support, bypassing search entirely.
Results & The Strategic Imperative
Early adopters of Intent-First architecture have seen significant improvements in user retention. When search works, users return. When it fails, they abandon the channel.
The conversational AI market is booming, but enterprises that continue to deploy standard RAG architectures will continue to fail. AI will confidently give wrong answers, users will abandon digital channels, and support costs will rise. Intent-First is not about better models; it’s about understanding what a user wants before you try to help them.
The demo is easy. Production is hard. But the pattern for production success is clear: Intent First.
