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The quality of your instructions directly determines the quality of the knowledge graph and AI responses. Vague instructions produce vague results. Specific, well-structured instructions produce graphs and answers you can trust.

Be specific about entity types

List the exact types you want extracted. Vague instructions leave Along guessing, and the graph will reflect that ambiguity.Don’t say “extract important people and organizations.” Say “Extract: Person (name, title, company), Organization (name, industry, headquarters).”

Specify exclusions explicitly

It’s often more effective to state what to skip than what to include. Along’s defaults are broad by design. Exclusions help you trim noise without having to enumerate every entity type you do want.

One instruction per concern

Break complex requirements into separate sentences rather than compound clauses. Long compound instructions are harder to follow consistently. “Extract deals and link them to their participants and their companies and their competitors but not internal stakeholders” is four separate rules — write them as four sentences.

Test incrementally

Add a small batch of representative content, inspect what the graph captures, then refine. Don’t try to write perfect instructions for content you haven’t seen yet. The graph’s entity and relationship counts are visible from the Safe dashboard — use them as feedback signals.

Good vs. bad example

Bad:
Good:
The bad example gives Along nothing actionable. The good example specifies types, relationships, and exclusions — each as a discrete instruction.

Common patterns

Citation enforcement
Scope limiting
Tone setting
Recency preference
Structured output format