Edit Prediction is Oppla’s low-latency, context-aware feature that surfaces likely next edits as you type or navigate code. It blends language-model completions with predictive heuristics so suggestions feel proactive and relevant to your current intent. This page is a practical stub with conceptual details, setup, and troubleshooting for engineers and power users. Full examples and UX screenshots will be added soon.

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What is Edit Prediction?

Edit Prediction predicts your next changes in-place — not just completing a token, but suggesting whole edits, ranges, and intent-aware transformations such as:
  • Completing multi-line constructs (loops, function bodies)
  • Proposing import additions and automatic reordering
  • Fixing off-by-one or common logic mistakes
  • Suggesting refactor steps (rename, extract function) as a preview
It is optimized for minimal latency so predictions appear while you type, and for correctness by using project-wide context.

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How it works (high-level)

  1. Context collection: Oppla constructs a lightweight context window from the active file, open buffers, and important project files (imports, configs).
  2. Local heuristics: Fast, local heuristics apply to produce immediate suggestions for trivial edits.
  3. Model scoring: If deeper reasoning is required, the configured model scores candidate edits and ranks them by confidence.
  4. Presentation: The top predictions are shown inline or in a suggestion strip; accept, cycle, or dismiss them.
Key considerations:
  • Privacy: By default, context sent to cloud models is minimized; local models avoid outbound traffic.
  • Safety: Predictions are suggestions — they require explicit acceptance to modify files unless configured otherwise.

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Quick start

  1. Open AI Settings: Command Palette → oppla: Open AI Settings (or Preferences → AI)
  2. Ensure an AI provider or local model is configured (see AI Configuration).
  3. Enable Edit Prediction:
    • Preferences → AI → Enable “Edit Prediction”
  4. Try it: Open a source file and start typing a function or a common pattern. Watch for inline predictions.

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Keyboard & UX

  • Accept inline prediction: Tab (configurable)
  • Accept in suggestion strip: Cmd-Enter / Ctrl-Enter
  • Next suggestion: Cmd-Right / Ctrl-Right
  • Previous suggestion: Cmd-Left / Ctrl-Left
  • Dismiss: Esc
  • Toggle Edit Prediction on/off: Command Palette → AI: Toggle Edit Prediction
You can customize these bindings in your keymap. See Key Bindings.

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Configuration (example)

Add or edit your user settings (~/.config/oppla/settings.json) to tweak how Edit Prediction behaves: 
{
  "ai": {
    "edit_prediction": {
      "enabled": true,
      "local_first": true,
      "max_suggestions": 3,
      "accept_key": "tab",
      "confidence_threshold": 0.65,
      "context_window_lines": 400,
      "prefetch_on_file_open": true,
      "auto_apply_low_risk": false
    }
  }
}
Field notes:
  • local_first: Prefer local runtime when available (reduces latency & improves privacy).
  • confidence_threshold: Minimum confidence to show a suggestion (0–1).
  • auto_apply_low_risk: When true, low-risk single-line fixes (e.g., missing semicolon) can be applied automatically — use with caution.

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Privacy & security

  • Default behavior minimizes remote context: only line ranges and a short surrounding window are sent to cloud providers unless you opt in to broader context.
  • For sensitive projects, enable local_first and run models locally (Ollama, llama.cpp, etc.).
  • Audit logs: Enterprise builds can record prediction requests for compliance — check Privacy & Security docs for configuration details.
  • Always review edits before accepting. Predictions are heuristics and may be incorrect.

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Developer & integration notes

  • Edit Prediction exposes an internal API for extensions to register custom predictors (planned). Extensions may provide domain-specific suggestions (e.g., SQL snippets, test generation).
  • Prediction scoring uses a hybrid model: a lightweight local scorer for immediate suggestions plus server/model scoring for higher-quality options.
  • If you’re building tooling that interacts with predictions, prefer non-blocking callbacks and provide opt-out settings to users.

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Troubleshooting

  • No suggestions appearing:
    • Confirm ai.edit_prediction.enabled is true.
    • Check your provider is configured and reachable (AI Configuration).
    • If using local models, ensure the local runtime is running and reachable.
  • Suggestions are low quality:
    • Increase context by opening relevant files (Edit Prediction prefers open buffers).
    • Try a different model or adjust confidence_threshold.
    • Disable any experimental rules that modify code structure.
  • High latency:
    • Enable local_first.
    • Reduce context_window_lines or set prefetch_on_file_open to false.
  • Unexpected auto-applies:
    • Ensure auto_apply_low_risk is false; if true, set to false while debugging.

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Best practices

  • Let Edit Prediction learn: use defaults for a week before heavy customization.
  • Use local_first on private codebases.
  • Combine with AI Rules to constrain behaviors (e.g., disallow certain automated edits).
  • Keep open only files relevant to the task — smaller context often leads to better suggestions.

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Feedback

This is a living feature. If Edit Prediction behaves unexpectedly or you have ideas for improvements (acceptance UX, confidence tuning, domain-specific predictors), please open a docs or feature request.
Note: This page is a functional stub. Full UX examples, GIFs, and benchmarked latency/accuracy numbers will be added after engineering provides measurement artifacts.