Prompt Engineering
The discipline of designing, testing, and optimizing the textual instructions given to an LLM to maximize the quality, accuracy, and consistency of its output.
In BasaltHQ, prompts are not ad-hoc strings typed by users. They are rigorously engineered artifacts, version-controlled and A/B tested like production code. Each agentic module (CRM outreach, legal review, ERP forecasting) ships with a library of battle-tested system prompts that define the agent's persona, constraints, output format, and error-handling behavior. These prompts incorporate few-shot examples drawn from real enterprise scenarios, ensuring the agent's behavior is predictable and aligned with corporate standards. Prompt regression testing is automated—if a model update degrades output quality on any benchmark prompt, the deployment is automatically blocked.
Related Concepts
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LLM Reasoning Chain
A structured sequence of logical steps an LLM follows to arrive at a verifiable conclusion, analogous to showing your work in mathematics.
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Context Window Management
The strategic allocation and optimization of the finite amount of text an LLM can process in a single inference call.
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Fine-Tuning
The process of further training a pre-trained LLM on a smaller, domain-specific dataset to specialize its behavior for a particular industry or task.