Mastering Gemini 1.5: Context Caching and Controlled Generation

Gemini 1.5 Pro’s 2-million token context window is a superpower, but managing that much data requires advanced patterns to keep costs low and responses reliable.

1. Context Caching (The Cost Killer)

If you are repeatedly querying the same large dataset (e.g., a 1000-page PDF manual or a massive codebase), you shouldn’t pay to send those tokens every time.

• Context Caching: Store the “pre-computed” tokens on Google’s servers.
• Impact: Reduces input token costs by up to 90% for repeated queries.

# Example: Creating a cache for a large documentation set
curl -X POST "https://generativelanguage.googleapis.com/v1beta/cachedContents" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "models/gemini-1.5-pro-002",
      "contents": [{ "parts": [{ "file_data": { "file_uri": "...", "mime_type": "application/pdf" } }] }],
      "ttl": "3600s"
    }'

2. Controlled Output (JSON Schema)

For production integrations, you cannot rely on “Please return JSON.” Gemini now supports Response Schema to enforce strict structure.

const schema = {
  type: "object",
  properties: {
    sentiment: { type: "string", enum: ["positive", "negative"] },
    confidence: { type: "number" },
  },
  required: ["sentiment"],
};

const model = genAI.getGenerativeModel({
  model: "gemini-1.5-pro",
  generationConfig: { responseMimeType: "application/json", responseSchema: schema },
});

3. Grounding with Google Search

Gemini can now “Ground” its answers in real-time Google Search results to reduce hallucinations and provide citations.

• Pattern: Enable google_search_retrieval as a tool.

4. Fine-tuning with Vertex AI

When prompts aren’t enough, Gemini allows you to fine-tune the model on your specific brand voice or specialized technical domain using the Google Cloud Vertex AI platform.

Summary

Gemini 1.5 Pro is more than just a large window; it’s a developer-first platform. By utilizing Context Caching for efficiency and Response Schemas for reliability, you can build enterprise AI applications that are both powerful and cost-effective.