How LLMs Retrieve Information from Blog Articles
Check our new infographic on how LLMs
AI & Machine Learning 2026
How LLMs Retrieve Information
Understanding the 4 core methods that power AI responses and decision-making
The Retrieval Pipeline
User Query
→
Intent Analysis
→
Method Selection
→
Response
1
Parametric Memory
Built-in knowledge from training
When Used:
General facts, concepts, established knowledge
Speed:
Instant (no external calls)
Limitation:
Fixed cutoff date, no real-time updates
Example: "Explain quantum computing" → Direct recall
2
Web Search
Real-time internet retrieval
When Used:
Current events, breaking news, live data
Process:
Query → API → Parse → Synthesize
Sources:
News sites, public databases, forums
Example: "Stock price of NVIDIA today" → Live search
3
RAG (Retrieval-Augmented Generation)
Document & knowledge base search
When Used:
Private docs, uploaded files, company data
Process:
Embeddings → Vector search → Retrieval
Sources:
Internal databases, user uploads, PDFs
Example: "Summarize our Q3 report" → Document search
4
Tool Use & Function Calling
External APIs & computation
When Used:
Calculations, code exec, specialized tasks
Tools:
Calculators, interpreters, weather APIs
Benefit:
Precise results for structured queries
Example: "Calculate 15% ROI over 5 years" → Calculator
Key Insights
Hybrid Approach
Complex queries combine multiple methods for comprehensive answers
Smart Selection
LLMs automatically choose optimal retrieval based on query intent
Cross-Verification
Multiple sources reduce hallucinations and improve accuracy
Trade-offs
Balance between speed, accuracy, freshness, and cost per method
