constraining

Type: kb/types/tag-readme.md · Status: current

Making semantics more focused by narrowing the space of valid interpretations an artifact admits — from partial narrowing (conventions) to full commitment (deterministic code). The primary mechanism for hardening deployed systems, with relaxing as its deliberate reverse. A child of learning-theory.

Definition and spectrum

• constraining — definition and spectrum: storing an output, writing a convention, adding structured sections, extracting deterministic code
• codification — the far end, where the medium itself changes from natural language to a symbolic artifact with formal semantics
• agentic systems interpret underspecified instructions — the foundation: the spec-to-program projection model, semantic boundaries, and the constrain/relax cycle

Instances and techniques

• storing LLM outputs is constraining — the simplest instance: keeping a specific output resolves underspecification to one interpretation
• constraining during deployment is continuous learning — versioned constraining beats weight updates on inspectability and rollback
• spec mining as codification — observe behavior, extract deterministic rules, grow the calculator surface monotonically
• error messages that teach are a constraining technique — in agent systems the error channel is an instruction channel
• methodology enforcement is constraining — review gates and validation as constraining applied to the KB's own methodology

Deciding and reversing

• codify-versus-LLM decision heuristics — four lenses on the codify-vs-LLM decision, with evidence they come apart at the edges
• specification strategy should follow where understanding lives — when to commit: before execution, during execution, or after repeated observation
• progressive constraining commits only after patterns stabilize — the default discipline: codify after the pattern proves the need
• unified calling conventions enable bidirectional refactoring — how to commit reversibly: neural and symbolic components behind the same callable interface
• codification and relaxing navigate the bitter lesson boundary — why reversibility matters: codification is a bet that may need relaxing
• operational signals that a component is a relaxing candidate — five testable signals for detecting when to reverse codification

Related Tags

• deploy-time-learning — the framework constraining serves; the verifiability gradient locates constrained artifacts
• distillation — the orthogonal mechanism: extraction rather than narrowing

Other tagged notes

• A methodology is agent-extensible only where it is closed under its own recommendations - Closure under its own recommendations — an agent extends a methodology only where it specifies its reason-vs-codify and verification decisions, bounded by verification cost
• Ad hoc prompts extend the system without schema changes - Any system with an LLM agent layer can absorb new requirements through natural language prompts without changing the deterministic base
• Constraining and distillation both trade generality for reliability, speed, and cost - Constraining narrows interpretation and distillation extracts focused artifacts; both can trade generality for reliability/speed/cost when task fit is good
• Enforcement without structured recovery is incomplete - The enforcement gradient covers detection and blocking but has no recovery column — recovery strategies (corrective → fallback → escalation) are the missing layer, and oracle strength determines which are viable at each level
• Fixed artifacts split into exact specs and proxy theories - Fixed artifacts are safe when their spec fully captures the problem; they are risky when they encode proxy theories whose components may not compose into the larger capability
• Inspectable artifact, not supervision, defeats the blackbox problem - Chollet frames agentic coding as ML producing blackbox codebases — codification counters this not by requiring human review but by choosing readable artifacts (code, prompts, schemas) that any agent can inspect, diff, test, and verify
• Legal drafting solves the same problem as context engineering - Legal drafting parallels context engineering because both write ambiguous natural-language specifications for judgment-based interpreters, but law develops constraining more than codification
• LLM↔code boundaries are natural checkpoints - At each LLM↔code transition both semantic underspecification and execution indeterminism collapse simultaneously, making these boundaries natural places to anchor debugging, testing, and refactoring
• Opacity is a scale threshold, not a class property - Opacity is not a representational form; any representation becomes practically opaque at sufficient scale, though distributed-parametric artifacts cross that threshold earliest.
• Underspecification and indeterminism complicate programming for prompts in distinct ways - Indeterminism doubles test runs (statistical testing over distributions); underspecification doubles test targets (spec analysis for ambiguity). Conflating the two leads to misdiagnosis