Smart Sourcing

Intelligent sourcing that requires citations only when the cost is justified by the value of verification.

Philosophy

Full sourcing is prohibitively expensive (10-16x token increase). Smart sourcing targets high-value claims where verification materially improves accuracy.

When to Source

REQUIRE Sources

Claim Type Example Why Source

Version numbers "Python 3.12 added..." Versions change, easy to verify

Performance claims "30% faster than..." Quantitative claims need evidence

Security recommendations "Use bcrypt for..." Security advice must be current

API specifications "The function accepts..." APIs change between versions

Release dates "Released in Q4 2025" Factual, verifiable

Pricing/limits "Free tier allows 1000 requests" Business terms change

Deprecated features "X was removed in version Y" Breaking changes need verification

DO NOT Require Sources

Claim Type Example Why No Source

General concepts "Async improves concurrency" Foundational knowledge

Code examples Demonstrative snippets Illustrative, not factual claims

Opinion/preference "Consider using..." Clearly framed as suggestion

Common knowledge "Git tracks changes" Universal understanding

Logical derivations "Therefore, X implies Y" Reasoning, not factual claim

Syntax "Use def for functions" Language documentation is stable

Decision Framework

Is this a factual claim? ├── No → No source needed └── Yes → Would being wrong cause harm? ├── No (minor inconvenience) → No source needed └── Yes → Is verification cost-effective? ├── No (>5k tokens for low-value claim) → State uncertainty └── Yes → Perform web search, cite source

Implementation

Inline Citations

When sourcing is warranted:

Python 3.12 introduced type statement for type aliases[1].

Sources:

1. PEP 695

Uncertainty Markers

When sourcing isn't cost-effective but uncertainty exists:

Python 3.12 introduced the type statement (verify in release notes for your version).

Confidence Levels

Confidence Action

High (stable facts) State directly

Medium (may have changed) Add verification note

Low (uncertain) Source or acknowledge uncertainty

Token Cost Analysis

Approach Token Cost Use Case

No sourcing Baseline General knowledge

Uncertainty marker +5-10 tokens Medium confidence

Single source +2-5k tokens High-value claim

Multiple sources +10-20k tokens Research tasks

Examples

Good: Source Version-Specific Claim

User: What's new in FastAPI 0.109?

Response: FastAPI 0.109 added Pydantic v2 support as default[1] and improved OpenAPI schema generation[2].

Sources:

1. FastAPI Release Notes
2. GitHub Release 0.109.0

Good: No Source for General Concept

User: How does async/await work?

Response: Async/await enables non-blocking I/O by allowing coroutines to yield control while waiting for operations. The event loop manages execution, resuming coroutines when their awaited operations complete.

Good: Uncertainty Marker

The connection pool default is typically 10 connections (verify in your database driver documentation as this varies by library and version).

Integration

Works with:

• conserve:response-compression

• Keeps sourced responses concise

• conserve:token-conservation

• Weighs source cost vs value

• memory-palace:research

• Full sourcing for knowledge corpus

When To Use Full Sourcing

When NOT To Use

• Internal project code that doesn't need citations

• Casual conversation without factual claims

Escalate to full sourcing (accept high token cost) for:

• Knowledge corpus entries (permanent documentation)

• Security advisories (safety-critical)

• Compliance/legal claims (audit requirements)

• Research tasks (user expects thorough investigation)

For these cases, use memory-palace:research workflow which is designed for comprehensive sourcing.