Context Injection

Context Injection

Context injection is the practice of dynamically inserting relevant information — documents, data, examples, or tool outputs — into an AI prompt so the model has the knowledge it needs to produce accurate, grounded responses. Effective injection is about more than pasting text; it requires deliberate placement, formatting, and token budget allocation to maximize the model's ability to use the injected material.

Workflow

1. Identify the Context Need: Analyze the task to determine what types of external information the model requires. A code review needs the source file; a support question needs product documentation; a personalized reply needs the user's profile. Clearly categorize each need as document grounding, few-shot examples, tool output, or metadata.

2. Gather the Context: Retrieve the necessary information from its source — a database, file system, API response, vector store, or prior conversation. Apply any necessary compression or truncation before injection so the material fits within the allocated token budget.

3. Select an Injection Strategy: Choose the appropriate injection method based on the context type and the model's attention patterns:

   • System prompt injection — persistent context like role definitions, rules, and user preferences go in the system message.
   • Document grounding — retrieved documents or files are inserted in the user message, typically before the question.
   • Few-shot examples — input/output pairs demonstrating the desired format are placed between the system prompt and the user query.
   • Tool output injection — results from function calls or API invocations are injected as assistant/tool messages in the conversation.

4. Format and Delimit the Context: Wrap injected content in clear delimiters (XML tags, markdown headers, or triple-backtick fences) so the model can distinguish instructions from context from the user's query. Label each section explicitly (e.g., <retrieved_document>, <user_profile>, <code_file>).

5. Assemble the Prompt: Combine the system prompt, injected context blocks, conversation history, and the current user query into the final prompt. Place the most critical context closest to the user's query (recency bias) and the most stable context (rules, persona) in the system message.

6. Validate Token Allocation: Confirm the total prompt fits within the model's context window with enough headroom for the expected generation length. If over budget, compress or remove the lowest-priority context blocks first.

Key Concepts

• Context Placement: Where context appears in the prompt matters. Models exhibit a "lost in the middle" effect — they attend most strongly to the beginning and end of the context window. Place the highest-priority information at the start of the context block or immediately before the query.
• Token Budget Allocation: A well-designed prompt allocates tokens deliberately: ~10–15% for system instructions, ~50–70% for injected context, ~5–10% for conversation history, and ~15–25% reserved for the model's response.
• Delimiters and Labeling: Clear delimiters (XML tags, markdown fences) help the model parse injected sections. Without them, the model may confuse context content with instructions, especially in long prompts.
• Few-Shot Examples: Injecting 2–3 input/output examples dramatically improves format compliance and task understanding. Place examples after the system prompt and before the actual query.

Usage

Use this skill whenever an AI model needs external information to complete a task accurately. Provide the raw context, the task description, and optionally a prompt template. The skill assembles a well-structured prompt with the context injected in the optimal position.

Examples

Example 1: Injecting a Code File for a Code Review Task

Prompt Template:

<system>
You are a senior software engineer performing a code review. Identify bugs,
security issues, and style violations. Be specific and cite line numbers.
</system>

<code_file path="src/api/users.py">
import sqlite3
from flask import Flask, request, jsonify

app = Flask(__name__)

def get_db():
    return sqlite3.connect("users.db")

@app.route("/users", methods=["GET"])
def list_users():
    db = get_db()
    query = f"SELECT * FROM users WHERE role = '{request.args.get('role')}'"
    results = db.execute(query).fetchall()
    return jsonify(results)

@app.route("/users/<int:user_id>", methods=["DELETE"])
def delete_user(user_id):
    db = get_db()
    db.execute(f"DELETE FROM users WHERE id = {user_id}")
    db.commit()
    return "", 204
</code_file>

Review the above code file. List all issues found.

Model Output:

1. SQL Injection (critical) — Lines 12–13: User input from request.args.get('role') is interpolated directly into the SQL query. Use parameterized queries: db.execute("SELECT * FROM users WHERE role = ?", (role,)).
2. SQL Injection — Line 19: user_id is interpolated into the DELETE query. Use a parameterized query.
3. No connection cleanup — get_db() opens a connection but it is never closed. Use a context manager or Flask's teardown_appcontext.
4. No authentication/authorization — The DELETE endpoint has no access control. Any caller can delete any user.