context-injection

You can ground responses by injecting external documents and few-shot examples directly into the prompt using specific delimiters and placement strategies. This ensures the model references the provided context to generate accurate, task-specific answers.

The best way to format retrieved documents for document-grounded Q&A is applying clear delimiters, structured placement rules, and token budgeting. This prevents lost-in-the-middle issues and ensures the large language model effectively consumes the injected context.

Manage token budget during context injection by applying compression techniques and calculating token allocation for system instructions, documents, and few-shot examples. This keeps the total prompt length within the model's context window while maximizing relevance.

Yes, context injection supports code review by inserting source files and system instructions into the prompt. The model analyzes the injected code structure and context to generate specific, grounded feedback rather than relying on pre-trained knowledge.

Models ignore context placed in the middle of prompts due to lost-in-the-middle attention issues. Context injection solves this by applying specific placement strategies that position critical documents and few-shot examples where model attention is highest.

Handle conflicting sources during context injection by applying built-in safety and edge-case guidance. The Skill includes strategies to resolve conflicting retrieved documents and provides fallback messages when necessary context is missing or contradictory.