Context Engineering Studio
Six strategies, one task, streaming in parallel — context engineering made visible
6 strategies streaming in parallel
TTFT per strategy
Token count per panel
Auto-detected winner
01The Problem
The phrase 'prompt engineering' has become synonymous with tricks and hacks — magic phrases, template libraries, cargo-culted patterns. The actual discipline is different: it's about what information goes into the context window, how it's structured, and how each element shapes the output distribution. I wanted to make that difference empirically visible — not as a claim, but as a live experiment you can run yourself.
02The Approach
Six API calls fire in parallel when you hit Run. Each implements a different context strategy via a different combination of system prompt and user message construction. All six stream independently to their panels. TTFT and token count are tracked per panel. After all streams complete, the 'Full Stack' panel (which combines all strategies) is highlighted as the winner — because it almost always is.
03Architecture Decisions
Parallel SSE streams from a single API route
The /api/stream route accepts a strategy name and constructs the appropriate system+user message combination. The frontend fires 6 simultaneous fetch calls (one per strategy) and routes each SSE stream to the correct panel via strategy-keyed state.
Strategy implementation as prompt construction
Each strategy is a different way of building the prompt, not a different model or temperature. Persona: 'You are a world-class expert.' Grounding: inject relevant facts before the task. Few-shot: prepend 2-3 examples in desired format. Constraints: explicit rules list. Full Stack: all of the above combined.
TTFT measurement at the token boundary
Time-to-first-token is recorded when the first content_block_delta event arrives from the Anthropic SSE stream. The done event includes total token count. This gives empirical data on which strategies add latency (almost none — it's all model generation time).
04Key Insight
The gap between baseline and full-stack scales with task complexity. For simple tasks (arithmetic, definitions), all strategies produce near-identical output — the baseline is fine. For complex tasks (architecture decisions, analysis, explanation), the gap is dramatic. The practical lesson: invest in context engineering proportionally to task difficulty.
05Why It Matters
The most timely demo in the portfolio — context engineering is the hottest concept in production AI (March 2026). Directly demonstrates the discipline that separates AI demos from production systems. Relevant for every AI engineering role: Clio (legal document context), Cohere (agentic context management), Giga (enterprise AI pipelines).