Product Development Lifecycle / Optimize
Optimizing The
Execution Unit.
99% of the industry talks about prompt engineering. Few have understood that the context surrounding a prompt is equally — if not more — relevant to the quality of the output. Swisper goes one step further: we don't optimize prompts in isolation. We optimize the Execution Unit.
“Context engineering is the delicate art and science of filling the context window with just the right information for the next step.”
Andrej Karpathy, former Sr. Director of AI, Tesla (June 2025)
The Execution Unit
The atomic unit of optimization in Swisper is the combination of four dimensions that together determine quality, cost, and performance of every AI interaction:
State
What information enters the context window for each interaction. Runtime variables, memory recalls, tool schemas, session history — every token counts. Context Management ensures agents receive exactly the right information without wasting the window on irrelevant data.
Prompt
Prompt Builder: Create and iterate on prompt templates with version history. State variable placeholders populated from runtime data. Collaborative editing across engineering and product teams. Prompt Optimizer: Opens any trace node from live production and recreates exact runtime conditions. Modify the prompt, swap models, inject state variables — before committing to any batch test. Other platforms show you the failure; The Lab lets you recreate the crime scene.
Model Selection
Access a range of model providers across cloud platforms. Compare candidates against real production scenarios — your actual workloads, your actual quality criteria. Swisper deliberately does not use the OpenAI-compatible standard as its default integration layer. While convenient, it forces every model into the same interface — losing the specific tools, skills, and controls that each provider offers. Swisper’s LLM Adapter connects to each provider natively, preserving the full power of every model.
Model Configuration
Temperature, top-p, max tokens, stop sequences — and the model-specific parameters that only exist for certain providers. Fine-tune behavior per use case, per agent, per Execution Unit.
Results Analyzer
Evaluates each Execution Unit across three dimensions:
Scenario Library:
Build and maintain libraries of production scenarios as test fixtures. Capture real user interactions. Reuse across prompt iterations and model migrations.
LLM as a Judge:
Model-based qualitative assessment for open-ended responses. Combine human scoring with automated evaluation. Teams prove which Execution Unit is better — not argue.