Intelligent Search Strategies
Scrapalot uses advanced Retrieval Augmented Generation (RAG) to find the most relevant information in your documents and deliver accurate, well-sourced answers. The system automatically selects the best search approach for each question, ensuring you get precise results every time.
How It Works
When you ask a question, Scrapalot analyzes what you're looking for and automatically chooses the optimal search strategy. You don't need to configure anything—the system adapts to your needs in real-time.
Intelligent Question Understanding
The system classifies your questions to find the best search method:
Question Types
Factual Questions - Direct questions with clear answers
- Example: "What is the capital of France?"
- Best for: Quick fact lookup
Conceptual Questions - Abstract ideas requiring deeper understanding
- Example: "Explain quantum entanglement"
- Best for: Learning new concepts
Relational Questions - Understanding how things connect
- Example: "How are modules X and Y connected?"
- Best for: Exploring relationships
Analytical Questions - Comparison and evaluation
- Example: "Compare Jung and Freud's theories"
- Best for: Understanding differences and similarities
Conversational Questions - Follow-up questions in context
- Example: "What about the other approach?"
- Best for: Natural conversation flow
Complexity Assessment
The system evaluates question complexity to deliver appropriately detailed answers:
| Complexity | Question Example | Answer Style |
|---|---|---|
| Simple | "What is the capital of France?" | Quick, direct fact |
| Moderate | "Papers from 2023 about AI ethics" | Filtered results with context |
| Multi-part | "Explain quantum entanglement" | Comprehensive explanation |
| Complex | "Compare Jung and Freud's theories" | In-depth analysis |
| Deep | "How did archetype theory influence modern psychology?" | Synthesized insights from multiple sources |
When to Use Different Search Approaches
For Exact Matches
What you need: Specific error codes, version numbers, dates, or IDs
Examples:
- "Show me error 221" (not 220 or 222)
- "Changes in v2.1.3" (not v2.1.2)
- "Reports from March 15, 2024"
How it works: Combines understanding your intent with precise keyword matching to find exactly what you specified.
Why it matters: Prevents getting similar but incorrect results—when you need error 221, you don't want error 220.
For Code and Technical Content
What you need: Specific syntax, commands, or technical patterns
Examples:
- "Find queries using JOIN"
- "Functions using async/await"
- "YAML files with ports: 8080"
How it works: Searches for exact technical terms while understanding the broader context.
Why it matters: Technical terms mean specific things—"JOIN" in SQL is different from general discussions about "combining" or "merging."
For Understanding Connections
What you need: How entities, concepts, or components relate to each other
Examples:
- "How are modules X and Y connected?"
- "What depends on service Z?"
- "Show the hierarchy from A to B"
How it works: Maps relationships between entities and traces connections through your knowledge base.
Why it matters: Finds hidden dependencies and relationships that keyword search would miss.
For Time-Specific Information
What you need: Documents from specific dates, versions, or time periods
Examples:
- "Documents from Q3 2024"
- "Code changes between v1.0 and v2.0"
- "Latest research papers"
How it works: Filters by date metadata while understanding temporal expressions like "latest" or "recent."
Why it matters: Gets you the right version or time period without wading through outdated information.
For Technical Terms and Acronyms
What you need: Domain-specific jargon that must match exactly
Examples:
- Finding "RAG" (not general retrieval discussion)
- "API" documentation (not general interface concepts)
- "CPU" specifications (distinct from "processor")
How it works: Balances exact keyword matching with semantic understanding.
Why it matters: Technical terms have precise meanings—substituting synonyms changes the meaning.
Search Quality Benefits
Accuracy Improvements
- 15-25% better results compared to generic search
- Prevents wrong answers by matching exact requirements when needed
- Finds hidden connections through relationship mapping
- Adapts automatically to question complexity
Speed and Efficiency
- Instant analysis of your question (typically under 1 second)
- Cached results for frequently asked questions
- Parallel processing for complex queries
- Progressive refinement if initial results aren't sufficient
Use Cases
Research and Learning
Best for: Understanding complex topics, comparing theories, exploring concepts
Example workflow:
- Ask: "Explain the main differences between Jung and Freud's psychology"
- System analyzes as complex comparative question
- Retrieves relevant content from both theorists
- Synthesizes comprehensive comparison
- Provides citations to source material
Benefits: Deep understanding with properly sourced information
Technical Troubleshooting
Best for: Finding specific errors, configuration issues, code examples
Example workflow:
- Ask: "How to fix error code 404 in the authentication module?"
- System recognizes exact match need (error 404)
- Filters to authentication context
- Returns precise solution
- Includes related documentation
Benefits: Fast, accurate problem resolution
Document Discovery
Best for: Finding documents by date, author, topic, or type
Example workflow:
- Ask: "Show me all reports from Q2 2024 about user engagement"
- System applies temporal filter (Q2 2024)
- Filters by topic (user engagement)
- Filters by type (reports)
- Returns matched documents with previews
Benefits: Quick access to exactly what you need
Exploratory Analysis
Best for: Understanding how concepts connect, discovering patterns
Example workflow:
- Ask: "How do different modules in the system interact?"
- System maps entity relationships
- Traces connections and dependencies
- Visualizes the network
- Highlights key interaction points
Benefits: Discover hidden patterns and dependencies
How Search Strategies Work Together
For complex questions, the system may combine multiple approaches:
Example: "How did Einstein's work influence modern quantum computing?"
- Factual search - Einstein's key discoveries
- Conceptual search - Quantum computing principles
- Relational search - How physics theories connect to computing
- Synthesis - Comprehensive answer showing the progression
Performance Characteristics
Response Times
| Search Complexity | Typical Response Time | What You Get |
|---|---|---|
| Simple fact lookup | < 1 second | Direct answer |
| Moderate complexity | 1-3 seconds | Detailed explanation |
| Complex analysis | 3-5 seconds | Comprehensive synthesis |
| Deep research | 5-10 seconds | Multi-source insights |
Quality Metrics
Accuracy: The system prioritizes correct answers over speed
- Validates information across sources
- Provides citations for verification
- Indicates confidence levels when uncertain
Relevance: Results match your actual intent
- Understands context from conversation
- Adapts to your knowledge level
- Filters out tangentially related content
Completeness: Comprehensive answers without overload
- Covers all aspects of your question
- Provides appropriate detail level
- Links to deeper information when available
Tips for Best Results
Write Clear Questions
Good: "What caused the authentication error on March 15th?" Why: Specific, includes context, clear intent
Less effective: "Something broke" Why: Vague, no context, unclear what you need
Use Natural Language
Good: "Show me recent papers about machine learning in healthcare" Why: Natural phrasing, clear filters, conversational
Less effective: "papers AND (machine_learning OR ML) AND healthcare AND date>2023" Why: No need for technical syntax—the system understands natural language
Provide Context
Good: "In the authentication module, how does password reset work?" Why: Specifies scope, easier to find relevant information
Less effective: "How does password reset work?" Why: Might search entire codebase instead of specific module
Ask Follow-up Questions
Good: "What about OAuth instead?" Why: System remembers you were discussing authentication
Less effective: Starting a completely new topic without context Why: Loses the conversation thread
What Makes This Different
Traditional Keyword Search
- Matches exact words only
- Doesn't understand intent
- Misses related concepts
- Returns too many irrelevant results
Scrapalot's Intelligent Search
- Understands what you mean
- Finds related concepts
- Adapts to question complexity
- Returns precisely what you need
Example: "How do I improve performance?"
Traditional search: Returns every document mentioning "improve" and "performance"
Scrapalot:
- Understands you want optimization techniques
- Considers your context (web app, database, etc.)
- Finds performance best practices
- Prioritizes actionable solutions
- Cites specific examples from your docs
Continuous Improvement
The search system learns from usage:
- Adapts to your document collection
- Improves relevance over time
- Refines understanding of your domain
- Optimizes for common question patterns
This intelligent search system ensures you spend less time searching and more time getting answers. The automatic strategy selection means you don't need to think about how search works—just ask your question naturally and get accurate, relevant results.