Hvordan fungerer egentlig Perplexitys live-søk? Forsøker å forstå arkitekturen

Daniel, your architecture guess is pretty close. Let me add detail:

The four-stage pipeline:

Stage 1: Query Processing

Not just keyword extraction:

• Tokenizes input
• Identifies entities, locations, concepts
• Detects ambiguity
• May reformulate into multiple search queries

Example: “Latest developments in quantum computing” →

• Intent: Recent information
• Topic: Quantum computing
• Time frame: Current/latest
• Search reformulation: “quantum computing 2025”, “quantum computing news”, etc.

Stage 2: Retrieval

Uses semantic search, not just keyword matching. A document about “artificial neural networks” can be retrieved for “deep learning” query because semantic meaning is similar.

The LLM layer deserves more analysis.

Model selection:

Perplexity uses multiple LLMs:

• GPT-4 Omni (for complex queries)
• Claude 3 (for certain tasks)
• Custom models (for efficiency)
• Users can select preferred model in Pro

How the LLM generates cited responses:

The LLM doesn’t just copy text. It:

1. Understands the query intent
2. Reads retrieved passages
3. Synthesizes a coherent answer
4. Attributes each claim to sources
5. Formats with citations

Example transformation:

Source 1: “Quantum computers use qubits which can exist in superposition.” Source 2: “Major players include IBM, Google, and IonQ.” Source 3: “Recent breakthroughs show 1000+ qubit processors.”

Perplexity output: “Quantum computers leverage qubits operating in superposition states [1]. Industry leaders IBM, Google, and IonQ [2] have recently achieved breakthroughs including 1000+ qubit processors [3].”

The synthesis creates new text while maintaining accurate attribution.

For content creators - here’s what matters for getting cited:

Source selection factors:

Format that gets cited:

Perplexity extracts information best from:

• Clear headings that signal topic
• Direct answers in first sentences
• Bulleted lists of facts
• Tables with data
• FAQ sections

What gets skipped:

• Vague introductions
• Content buried in dense paragraphs
• Promotional language
• Claims without supporting data

Quick Search vs Pro Search - the technical difference:

Quick Search:

• Single focused retrieval
• ~5 sources consulted
• Fast response (2-3 seconds)
• Best for simple factual queries

Pro Search:

• Multi-step retrieval
• Query decomposition
• May ask clarifying questions
• 10+ sources consulted
• Slower but more comprehensive
• Better for complex research

The decomposition:

Pro Search breaks complex queries into sub-queries:

“Best CRM for healthcare startups with HIPAA compliance” becomes:

• “CRM software healthcare”
• “HIPAA compliant CRM”
• “CRM startup pricing”
• “Healthcare CRM features”

Each sub-query retrieves different sources, then results are combined.

Hallucination prevention in Perplexity:

How it reduces hallucinations:

1. Citation requirement - Can’t generate uncited claims
2. Real-time retrieval - Current data, not just training
3. Multi-source corroboration - Important facts need multiple sources
4. Source credibility weighting - Reputable sources prioritized

The limitation:

Perplexity can still hallucinate if:

• Sources themselves are wrong
• Retrieval returns irrelevant docs
• Query is misunderstood

Compared to ChatGPT:

The forced citation mechanism is Perplexity’s main defense against hallucinations.

The contextual memory system:

Within a session:

Perplexity remembers conversation history:

• Previous questions encoded
• Context carries forward
• Follow-ups understand references

Example: Q1: “What are the latest developments in quantum computing?” Q2: “How does this compare to classical computing?”

For Q2, Perplexity understands “this” refers to quantum computing from Q1.

The attention mechanism:

Uses attention weights to determine which previous context is relevant to new query. Not everything carries forward - only contextually relevant parts.

The limitation:

Memory is session-based only. Close the conversation = context lost. No persistent personalization across sessions.

This is a privacy choice, not a technical limitation.

Focus Mode is underrated for understanding Perplexity’s architecture:

Available focuses:

What this reveals:

Focus Mode shows Perplexity can restrict its retrieval to specific source pools. This means they have:

1. Indexed and categorized sources
2. Separate retrieval systems per category
3. Ability to filter by domain type

For optimization:

If you want academic citations - make sure your research is indexed in academic databases. If you want general citations - focus on web-discoverable content.

This thread filled in the gaps in my understanding. Here’s my updated architecture diagram:

Perplexity Live Search Pipeline:

User Query
    ↓
Stage 1: Query Processing
├── NLP tokenization
├── Intent classification
├── Entity extraction
├── Query reformulation (multiple sub-queries)
    ↓
Stage 2: Information Retrieval
├── Semantic search (embedding-based)
├── API calls to web index
├── Source filtering (Focus Mode)
├── Passage extraction
├── Relevance ranking
    ↓
Stage 3: Answer Generation
├── Context window population
├── LLM synthesis (GPT-4/Claude)
├── Inline citation tracking
├── Conflict resolution
    ↓
Stage 4: Refinement
├── Fact-checking against sources
├── Coherence evaluation
├── Follow-up suggestion generation
├── Citation formatting
    ↓
Final Output (Answer + Citations + Suggestions)

Key insights:

1. Semantic retrieval - Not keyword matching, but meaning matching
2. Forced citations - Every claim tied to source, reduces hallucinations
3. Real-time index - Content can appear within hours of publication
4. Multi-model architecture - Different LLMs for different purposes
5. Session memory - Context awareness within conversations

For content optimization:

To get cited in Perplexity:

• Write in extractable format (lists, tables, direct answers)
• Include credibility signals (author, institution)
• Keep content fresh (update dates matter)
• Be the authoritative source on your topic

Thanks everyone for the technical deep dive.