How to Build a User Preference Engine for AI
Before You Start
A preference engine sits between your AI application and your memory layer. You need a way to store structured data that persists across sessions (a memory API like Adaptive Recall, a database, or a hybrid) and a way to inject retrieved preferences into your AI's context (system prompt modification, context augmentation, or a middleware layer). The steps below work with any storage backend, though the examples use Adaptive Recall's memory API because it handles confidence scoring and lifecycle management natively.
You should also decide upfront what level of personalization your application needs. A coding assistant benefits from remembering language preferences, framework choices, and explanation style. A customer support bot benefits from remembering product context, communication tone, and past issues. A general-purpose assistant benefits from all of the above. The categories you choose determine how complex your observation layer needs to be.
Step-by-Step Implementation
Start by listing every type of preference that would meaningfully change your AI's behavior. Group them into four natural categories. Communication preferences cover tone, detail level, explanation style, and terminology. Domain preferences cover technologies, frameworks, industry, and expertise level. Behavioral preferences cover interaction patterns like step-by-step vs complete solutions, code-first vs explanation-first, and iteration expectations. Negative preferences cover things the user has explicitly rejected or consistently ignored.
For each category, define which preferences are categorical (Python vs JavaScript, formal vs casual) and which are continuous (detail level from 1-10, expertise from beginner to expert). Categorical preferences are easier to store and apply. Continuous preferences require threshold decisions about when a value is different enough to change behavior.
// Example preference category definitions
const PREFERENCE_CATEGORIES = {
communication: {
tone: { type: 'categorical', values: ['formal', 'casual', 'technical'] },
detail_level: { type: 'continuous', min: 1, max: 10, default: 5 },
explanation_style: { type: 'categorical', values: ['examples-first', 'theory-first', 'code-only'] },
},
domain: {
primary_language: { type: 'categorical', values: null }, // open-ended
frameworks: { type: 'list', max_items: 10 },
expertise_level: { type: 'categorical', values: ['beginner', 'intermediate', 'advanced', 'expert'] },
},
behavioral: {
response_format: { type: 'categorical', values: ['step-by-step', 'complete-solution', 'outline-then-detail'] },
iteration_style: { type: 'categorical', values: ['one-shot', 'iterative', 'conversational'] },
},
negative: {
rejected_suggestions: { type: 'list', max_items: 50 },
avoided_topics: { type: 'list', max_items: 20 },
}
};Each preference record needs enough structure to support confident retrieval and graceful evolution. At minimum, store the preference category, the specific preference key, the value, a confidence score, a context qualifier (when this preference applies), the timestamp of the last supporting observation, and a counter of how many observations support this preference.
// Example preference object
{
"user_id": "user_abc123",
"category": "domain",
"key": "primary_language",
"value": "Python",
"confidence": 0.85,
"context": "data-science", // applies when working on data science topics
"observations": 12, // 12 interactions supported this preference
"last_observed": "2026-05-10T14:30:00Z",
"created": "2026-04-15T09:00:00Z"
}The context field is important because many preferences are situational. A user might prefer Python for data work and TypeScript for web work. Without a context qualifier, the system would randomly apply one or the other depending on which was stored most recently. With context qualifiers, the retrieval layer can match the current interaction context to the right preference.
If you are using Adaptive Recall, preferences map naturally to memories with entity connections. The preference value is the memory content, the confidence field maps to Adaptive Recall's built-in confidence scoring, and the context qualifier maps to entity tags that link the preference to relevant topics in the knowledge graph.
The observation layer monitors user interactions and extracts preference signals. It needs two extraction paths: one for explicit preferences and one for implicit patterns.
Explicit preference extraction watches for direct statements. When a user says "I prefer TypeScript" or "always use async/await" or "skip the basic explanations," the observation layer should recognize these as preference declarations and store them with high initial confidence (0.7-0.9). The simplest implementation uses the AI itself to identify preference statements by including a preference extraction instruction in your system prompt or post-processing pipeline.
// Post-interaction preference extraction prompt
const EXTRACTION_PROMPT = `Analyze the following user message for preference signals.
Return a JSON array of preferences found (empty array if none).
Each preference object should have:
- category: communication | domain | behavioral | negative
- key: the specific preference dimension
- value: the preference value
- confidence: 0.0-1.0 based on how clearly stated
- context: topic or situation this applies to (null if universal)
Only extract preferences the user clearly stated or strongly implied.
Do not infer preferences from a single ambiguous signal.
User message: {message}`;Implicit preference extraction requires tracking patterns across multiple interactions. This is more complex because any single behavior could be coincidence, but repeated patterns reveal genuine preferences. Track choices the user makes when presented with options (short vs long examples, which suggestions they accept vs ignore, how they modify your output). After a pattern appears in five or more interactions, promote it to a stored preference with moderate confidence (0.4-0.6) and let further observations increase or decrease that confidence.
Confidence scoring determines how strongly each preference influences the AI's behavior. Start every preference with an initial confidence based on how it was acquired: explicit statements start at 0.7-0.9, implicit patterns start at 0.4-0.6. Then update confidence with each new observation.
When a new observation corroborates an existing preference, increase confidence by a small increment that diminishes as confidence approaches 1.0. This prevents confidence from shooting to maximum after just a few observations while still allowing well-established preferences to reach high confidence over time.
function updateConfidence(current, corroborating) {
if (corroborating) {
// Diminishing returns as confidence approaches 1.0
const increment = (1.0 - current) * 0.15;
return Math.min(current + increment, 0.99);
} else {
// Contradictions reduce confidence more aggressively
const decrement = current * 0.25;
return Math.max(current - decrement, 0.01);
}
}When a new observation contradicts an existing preference (the user explicitly rejects something they previously preferred, or consistently behaves contrary to a stored pattern), decrease confidence aggressively. If confidence drops below a threshold (0.2 is reasonable), archive the preference and start observing for a replacement. Contradictions should reduce confidence faster than corroborations increase it, because acting on a wrong preference is more damaging than being slow to adopt a correct one.
At the start of each AI interaction, query the preference store for preferences relevant to the current context. Sort by confidence and relevance, take the top N preferences (typically 5-15, depending on your context window budget), and format them for injection into the AI's system prompt.
The retrieval query should consider both the topic of the current interaction and the preference categories that are universally relevant. Communication preferences (tone, detail level) apply to every interaction and should always be retrieved if they exist. Domain preferences should be filtered by the current topic context. Behavioral preferences should be retrieved based on the type of task (coding task, explanation, debugging). Negative preferences should always be checked to prevent repeat mistakes.
async function getRelevantPreferences(userId, currentContext) {
// Always retrieve universal preferences
const communication = await getPreferences(userId, 'communication', null, 0.3);
const negative = await getPreferences(userId, 'negative', null, 0.2);
// Retrieve context-specific preferences
const domain = await getPreferences(userId, 'domain', currentContext.topic, 0.3);
const behavioral = await getPreferences(userId, 'behavioral', currentContext.taskType, 0.3);
// Combine, sort by confidence, take top 15
const all = [...communication, ...negative, ...domain, ...behavioral];
all.sort((a, b) => b.confidence - a.confidence);
return all.slice(0, 15);
}
function formatForInjection(preferences) {
if (preferences.length === 0) return '';
const lines = preferences.map(p =>
`- ${p.category}/${p.key}: ${p.value} (confidence: ${p.confidence.toFixed(2)})`
);
return `\nUser preferences (adapt your response accordingly):\n${lines.join('\n')}\n`;
}Preferences go stale. A user's preferred framework six months ago may not be their preferred framework today. Implement three lifecycle mechanisms: temporal decay, consolidation, and contradiction detection.
Temporal decay gradually reduces the confidence of preferences that have not been reinforced recently. If a preference has not been observed in 30 days, reduce its confidence by a small percentage. If it has not been observed in 90 days, reduce it more aggressively. This prevents outdated preferences from persisting indefinitely while keeping actively reinforced preferences stable.
Consolidation merges related preferences into higher-level patterns. If a user has separate preferences for "uses React," "uses Next.js," and "uses TypeScript," consolidation can create a composite preference for "modern React/Next.js/TypeScript stack" that is more efficient to retrieve and apply. Adaptive Recall handles this through its built-in consolidation pipeline, which detects related memories and merges them automatically.
Contradiction detection identifies when new observations conflict with stored preferences and triggers resolution. When the system detects a contradiction, it should reduce confidence on the existing preference, store the new observation as a competing preference, and let subsequent observations determine which preference wins. Never silently overwrite a high-confidence preference with a single contradictory observation.
Testing Your Preference Engine
Test preference engines by simulating multi-session user journeys. Create test scenarios where a simulated user expresses preferences (some explicit, some implicit), interacts across multiple sessions, changes preferences over time, and contradicts previous preferences. Verify that the engine captures explicit preferences with high confidence, builds implicit preferences after sufficient observations, applies preferences correctly to AI output, handles preference changes gracefully without clinging to outdated data, and respects negative preferences consistently.
The most important test is the returning user test: after ten simulated sessions, does the AI's behavior meaningfully differ from its behavior for a new user? If the preference engine is working, the returning user should receive noticeably better-adapted responses.
Adaptive Recall provides preference storage, confidence scoring, and lifecycle management out of the box. Store user preferences as memories and let cognitive scoring handle retrieval and evolution.
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