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| title: AI Steering | |
| emoji: 🧭 | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.9.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # Core AI Steering Skills | |
| Interactive, **fully offline** demo of four practical techniques for steering | |
| LLM applications — no API key required, no live model calls: | |
| - **Reasoning Enhancement** — Chain of Thought and Tree of Thoughts prompting | |
| - **Context Window Management** — conversation buffers that condense instead of forget | |
| - **Semantic Guardrails** — keyword prefilter (real code) + semantic classification | |
| - **Model Routing** — a cheap classifier routes requests to the matching model tier | |
| - **Output Guardrails** — regex PII redaction of model responses (real code) | |
| - **Cost Tracking** — per-tier pricing showing what routing saves vs. all-Opus (real code) | |
| This Space runs in **simulated mode**: local Python heuristics stand in for | |
| the Claude API calls the real `ai_steering` package makes, so you can explore | |
| the mechanics of each skill for free. The guardrail keyword prefilter, PII | |
| redaction, and cost tracker are the actual production code, since they're | |
| designed to never need a model call. | |
| Source: https://github.com/aabhimittal/ai-steering — the GitHub package is | |
| fully Claude/Anthropic-backed; set `ANTHROPIC_API_KEY` and run `examples/*.py` | |
| locally to see live model output. | |