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

code

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:

markdown

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

Sources:
1. [PEP 695](https://peps.python.org/pep-0695/)

Uncertainty Markers

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

markdown

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

markdown

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](https://fastapi.tiangolo.com/release-notes/)
2. [GitHub Release 0.109.0](https://github.com/tiangolo/fastapi/releases/tag/0.109.0)

Good: No Source for General Concept

markdown

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

markdown

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.