Stop Treating Your Users Like Strangers: Identity Resolution for RAG

Most RAG implementations today feel like meeting a shopkeeper with a ten-second memory. You ask a question, get a technically accurate answer, and then have to explain your history again. While we have mastered vector search with tools like Vespa and pgvector, we are failing at identity resolution. Implementing proper identity resolution for RAG is the only way to move past generic, amnesiac interfaces that treat every user like a fresh session ID.

Your data stack—Kafka, dbt, and RudderStack—is a goldmine of intent. Yet, we usually strip this context away before hitting the LLM, forcing models to work in the dark. There is no reason to ask a user to describe a problem when their last five API errors are already logged in your event stream. We need a Behavioral Context Layer: a bridge between anonymous event streams and the authenticated records that define a user.

This article moves past basic text chunking to explore using RudderStack Profiles to build an identity graph that feeds directly into retrieval logic. It is time to stop guessing and start using existing data to make RAG actually smart.

The Goldfish Problem in Modern RAG

Most RAG setups suffer from persistent amnesia. Despite optimized embeddings, systems treat every query as a "cold start," ignoring behavioral data in event streams. Relying solely on query strings forces the system to ignore customer relationships, making sophisticated AI act like a stranger.

Stateless systems are boring systems

Statelessness limits AI utility. If a developer encounters repeated 401 errors and asks "How do I fix this?", a generic response on "Authentication Basics" is insufficient. By leveraging event data, the system should recognize the user is struggling with specific OAuth scopes. True agentic personalization shifts retrieval from simple semantic searches to filtered lookups based on real-time state. Without distinguishing between a VIP customer and a first-time visitor via Kafka topics or clickstream data, you are building a search bar, not an assistant.

Why manual metadata hacking fails

Teams often attempt to solve this by tagging vector embeddings with static metadata, such as user_id or plan_type. However, user behavior is fluid. Interests shift based on recent interactions in a frontend or updates via Debezium. Static metadata cannot capture session intent in real-time. Hard-coding these traits creates maintenance debt and a lag between action and reaction. If a user is browsing documentation for Vector Search, the system must prioritize that context over globally popular results.

# The "Goldfish" approach: Purely semantic, zero context
results = vector_db.search(
    query_vector=embed("How do I fix the lag?"),
    limit=5
)

# The Identity-Aware approach: Filtering by recent behavioral state
# The 'user_context' is pulled from your identity graph (e.g., RudderStack Profiles)
user_context = identity_service.get_latest_profile(user_id) 

results = vector_db.search(
    query_vector=embed("How do I fix the lag?"),
    filter={
        "tech_stack": user_context.last_seen_tech, # e.g., "Vespa.ai"
        "experience_level": user_context.trait_score
    },
    limit=5
)

Manually managing these filters at scale is unsustainable. Systems must utilize an identity graph to stitch behavioral data into a dynamic Context Layer, automating retrieval logic before it hits the LLM.

Building the Identity Resolution for RAG Pipeline

Raw event streams are fragmented. A user may browse docs anonymously on mobile (anonymous_id: abc-123) before logging in on a laptop (user_id: user_99). To a standard vector database, these are two different people. Without identity resolution, your LLM remains stuck talking to a stranger even when a known customer asks a question. We need a way to tie every click and support ticket to a single persistent key, creating a "memory bank" for the RAG system.

Stitching anonymous sessions with RudderStack Profiles

Avoid writing custom, brittle stitching scripts. Instead, use RudderStack Profiles to build a warehouse-native identity graph on Snowflake, BigQuery, or Databricks. This transparent layer maps disparate anonymous_ids to a single rudder_id. This allows the RAG system to reconstruct the user's journey. If a user spent twenty minutes on "Enterprise Security" docs while anonymous, the identity graph identifies exactly what "this" they are referring to when they later ask, "Is this compliant?"

The result is a "Context Snapshot"—a JSON summary of the user's identity and recent activity used to prep the RAG prompt:

# Fetching the stitched identity and recent behavioral traits
def get_behavioral_context(user_id):
    # This queries the warehouse-native profile built by RudderStack
    profile = db.query("""
        SELECT 
            traits.plan_level,
            traits.last_viewed_feature,
            identity_graph.all_anonymous_ids
        FROM profiles_table
        WHERE user_id = %s
    """, (user_id,))
    
    return {
        "tier": profile['plan_level'],
        "context": f"User was recently looking at {profile['last_viewed_feature']}"
    }

The identity graph as a retrieval filter

Identity resolution prevents "blind" vector searches. Sending a query to a vector DB and hoping for relevance is inefficient. If a user is on a "Basic" tier, they should not see "Enterprise" configuration steps, which clutter the context window and cause hallucinations. By using the identity graph as a filter, we prune the search space. We tell the vector DB to find documents matching the query that also align with the user's specific tier and recently used SDKs. This keeps the context window lean and makes responses feel personal.

Engineering the Behavioral Context Layer

To inform LLM responses in under 200ms, we must push resolved identity attributes directly into the vector store’s metadata. We need a "behavioral snapshot" that informs the retrieval engine without the latency of a heavy relational join.

From event stream to vector store filters

When a user interacts with an app, those events should immediately update how the RAG system perceives them. RudderStack captures events—clicks or API calls—and pipes them through a transformation layer. This layer enriches the event with traits from the identity graph, which then sync to low-latency stores like Vespa or pgvector. This creates a dynamic sandbox for search. Here is how to structure a filtered retrieval using Vespa's YQL:

# Example: Filtering RAG retrieval based on behavioral traits
import requests

def get_contextual_docs(query_embedding, user_traits):
    # We pull traits like 'preferred_language' and 'account_tier' 
    # from our real-time identity store
    language_filter = user_traits.get('last_browsed_language', 'python')
    tier_filter = user_traits.get('subscription_tier', 'free')

    yql_query = (
        "select * from docs where {targetHits: 5}nearestNeighbor(embedding, q_emb) "
        "and language contains @lang and access_level contains @tier"
    )

    body = {
        "yql": yql_query,
        "ranking.features.query(q_emb)": query_embedding,
        "lang": language_filter,
        "tier": tier_filter
    }
    
    response = requests.post("https://vespa-instance/search/", json=body)
    return response.json()

This approach narrows the search space using real-time data, reducing noise and hallucinations while improving answer speed.

Real-time context without the lag

To maintain performance, treat your vector store as a read-optimized view of the identity graph. Use a lightweight, event-driven pipeline to update user profiles in the vector store as actions occur. When a user logs in or upgrades their plan, the identity resolution engine fires an update. By the user's next question, the Behavioral Context Layer is already primed. We move from static retrieval to a model where data is constantly reshaped by the user's journey.

Closing the Loop: When Identity Informs Execution

The real payoff occurs when identity-aware context flows into the execution layer—the tools and agents performing the work. A customer data platform for AI functions as a real-time brain for your LLM.

Personalized tool selection for agents

Agents usually rely on thin tool descriptions. Injecting behavioral context allows for better tool selection. If a user has been hitting 500 errors on a Laravel route, the agent should prioritize 'System Logs' over 'General FAQ'. We pass these "intent signals" into the agent's reasoning loop, providing a command to skip irrelevant documentation.

// Example of injecting behavioral context into a tool-calling logic
$userProfile = RudderStack::profiles()->search($userId);

$agentContext = [
    'last_seen_page' => $userProfile->last_event['page_url'],
    'technical_proficiency' => $userProfile->traits['dev_score'],
    'active_incident' => $userProfile->computed_traits['is_struggling_with_config'],
];

// The LLM now knows to skip the 'Onboarding' tool 
// and go straight to the 'Deep Debugger' tool.
$response = $aiAgent->executeTask($userQuery, $agentContext);

The shift from retrieval to autonomous action

The industry is moving toward autonomous agents. However, action is risky without safeguards. Identity resolution serves as a primary guardrail. The system can verify if the user's session history matches the requested action. If a user asks to "delete the project" but has spent the last five minutes looking at "how to rename a project," the agent can pause. It recognizes the discrepancy between the vector of the question and the identity-history, allowing for logic orchestrated by a living history.

Conclusion

Stop building AI features that forget users every time the session refreshes. By bridging the gap between RudderStack event streams and your vector database, you move from a generic search tool to a system with memory. Using identity stitching to filter retrievals makes the warehouse the brain of your application. In AI-native development, personalization is the baseline. It is time to end the amnesia and build systems that recognize a familiar face.

Sources & Further Reading

• - YouTube — youtube.com
• The future of personalization — rudderstack.com
• How Fintechs Use RAG for Customer Personalization? | TechTose — techtose.com
• Data-Driven Personalization for Unified CX — evam.com
• How does RAG personalize customer experiences in retail — alexgenovese.com
• vespa.ai — vespa.ai

Frequently Asked Questions

What is identity resolution for RAG?

Identity resolution for RAG is the process of stitching disparate user identifiers, like anonymous session IDs and authenticated records, into a unified profile. This allows RAG systems to access a user's full behavioral history rather than treating every query as a cold start. By utilizing an identity graph, the system provides personalized context to the LLM, ensuring responses are tailored to the user's specific journey and historical interactions.

How does an identity graph improve retrieval accuracy?

An identity graph allows RAG systems to apply dynamic filters to vector searches based on real-time user traits, such as their technical proficiency or subscription tier. Instead of a broad semantic search that might return irrelevant 'Enterprise' results to a 'Basic' user, identity-aware retrieval prunes the search space. This specific filtering keeps the context window lean, reduces the likelihood of LLM hallucinations, and ensures higher relevance in the retrieved documents.

Why shouldn't I just use static metadata for user context?

Static metadata tagging in vector databases is brittle and cannot keep up with fluid user behavior. Interests and technical needs shift rapidly based on recent website interactions or API errors logged in event streams. Relying on static tags creates significant maintenance debt and a lag in personalization. A behavioral context layer driven by identity resolution captures real-time intent, allowing the RAG system to adapt instantly as the user moves through the application.

What is the 'Goldfish Problem' in modern RAG systems?

The Goldfish Problem refers to the persistent amnesia found in most RAG implementations. Even with optimized vector databases, these systems typically treat every query as a fresh session, ignoring the rich intent data stored in event streams like Kafka or RudderStack. This lack of memory forces users to repeatedly explain their context, making even sophisticated AI agents feel like strangers rather than helpful assistants who understand the user's recent history.