UNDERSTAND THE ISSUE
AI code generators and reviewers frequently suggest outdated versions for pnpm packages, GitHub Actions, LLM models, and Node.js. This occurs because the model's training data cutoff causes it to perceive newer, valid versions as hallucinations, defaulting to the latest version it "remembers."
DETERMINE APPROACH
Implement a deterministic validation layer and context-injection step to prevent the AI from downgrading dependencies and locking core runtime versions.
To address this deterministically within an autonomous CI/CD loop, we should decouple the version validation from the LLM entirely using a three-pronged approach:
- Pre-Flight Context Enrichment: Inject current stack versions into the AI reviewer's system prompt prior to execution so it has factual grounding.
- Post-Generation Guardrails: Run a script using a semantic versioning library to compare the
HEAD version against the AI's proposed version. Automatically strip downgrades.
- Hard File/Line Locks: Implement a regex or AST-based block that monitors the Node version specifically.
SPECIFY SCOPE
- Version Change Detection in
package.json, .github/workflows/, and config files.
- Deterministic validation script checking against npm/GitHub.
- Context injection updates for
td_cli prompt builder.
- Strict locks on
.nvmrc and engines.node.
DEFINITION OF DONE
- Pipeline intercepts and parses AI-proposed version diffs.
- Downgrades are automatically rejected via semantic version comparison.
- System prompt injects the latest supported versions.
- Hard block on Node.js version modification unless explicitly overridden.
UNDERSTAND THE ISSUE
AI code generators and reviewers frequently suggest outdated versions for pnpm packages, GitHub Actions, LLM models, and Node.js. This occurs because the model's training data cutoff causes it to perceive newer, valid versions as hallucinations, defaulting to the latest version it "remembers."
DETERMINE APPROACH
Implement a deterministic validation layer and context-injection step to prevent the AI from downgrading dependencies and locking core runtime versions.
To address this deterministically within an autonomous CI/CD loop, we should decouple the version validation from the LLM entirely using a three-pronged approach:
HEADversion against the AI's proposed version. Automatically strip downgrades.SPECIFY SCOPE
package.json,.github/workflows/, and config files.td_cliprompt builder..nvmrcandengines.node.DEFINITION OF DONE