Practical AI Agent Layer / nlp-experiments-backlog

NLP experiment backlog

A future backlog for sentiment classification, tokenization comparisons, semantic search, RAG, and fine-tuning comparisons.

shellbackend needed later
Focus
  • text classification
  • tokenization
  • semantic search
  • fine-tuning comparisons
Future feature idea

Experiment cards for sentiment classification, tokenization, semantic search, RAG, and fine-tuning comparisons.

Self-check
  1. What is the core job of "NLP experiment backlog"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.