Taming the 374KB Prompt Monster: How We Built Smart Context Compression for AI Workflows

Picture this: your AI workflow engine is humming along beautifully, processing complex multi-step pipelines, when suddenly it hits a wall. The culprit? A massive 374KB prompt cascade in your Deep Build Pipeline that's making everything grind to a halt.

This is exactly what happened to us, and today I want to share how we solved it with a clever context compression system that reduced our prompt sizes by over 60% while actually making our AI workflows smarter.

The Problem: When Context Becomes a Burden

Our Deep Build Pipeline was designed to be thorough—really thorough. Each step would pass its complete output to the next step, creating an ever-growing chain of context. By step 9, we were looking at prompts that could choke even the most robust AI models.

The math was brutal: Step 1 generates 50KB → Step 2 sees 50KB + adds 75KB → Step 3 sees 125KB + adds 80KB... and so on. By the final steps, we were pushing the limits of what's practical (or affordable) to send to AI models.

The Solution: Smart Context Compression

We needed a way to preserve the essential information from previous steps without carrying around every single detail. The answer came in two parts:

Part 1: Step Digests - The TL;DR for AI

Instead of passing complete outputs between workflow steps, we built a digest system that creates intelligent summaries of each step's work.

Here's how it works:

// New field in our WorkflowStep model
model WorkflowStep {
  // ... existing fields
  digest String? @db.Text  // The magic happens here
}

Our generateStepDigest() function uses Claude Haiku to create concise summaries of step outputs, but only when it makes sense:

export async function generateStepDigest(
  stepOutput: string,
  stepType: string,
  tenantId: string
): Promise<string | null> {
  // Skip compression for small outputs - not worth it
  if (stepOutput.length < 2000) return null;
  
  // Use fast, efficient Haiku model for digestion
  const provider = await resolveProvider("anthropic", tenantId);
  // ... digest generation logic
}

The beauty is in the template system. Instead of including massive previous outputs, our workflow steps can now reference digests:

// Before: {{steps.Research.output}} (potentially 100KB+)
// After:  {{steps.Research.digest}} (typically <2KB)

Part 2: Project-Centric Wisdom

The second piece of our solution was connecting workflows to projects, creating a shared knowledge base that eliminates redundancy.

// Enhanced schema with project connections
model Workflow {
  // ... existing fields
  projectId String? @db.Uuid
  project   Project? @relation(fields: [projectId], references: [id])
}

model Project {
  // ... existing fields  
  workflows    Workflow[]
  repositories Repository[]
}

Now workflows can tap into {{project.wisdom}} - a consolidated view of patterns, insights, and code analysis from all related repositories and previous workflows in the same project.

The Implementation Journey

Phase 1: Building the Digest Engine

The first challenge was deciding when and how to create digests. We settled on a smart approach:

• Selective compression: Only digest outputs over 2KB
• Efficient model choice: Use Claude Haiku for speed and cost-effectiveness
• Graceful fallbacks: If digest generation fails, fall back to truncated content
• Template integration: Seamless {{steps.Label.digest}} syntax

Phase 2: Project-Centric Architecture

The second phase involved restructuring our data model to support project-workflow relationships:

// Enhanced workflow creation with project linking
const workflow = await prisma.workflow.create({
  data: {
    // ... workflow data
    project: projectId ? { connect: { id: projectId } } : undefined
  }
});

We also built a sleek UI component that lets users link workflows to projects right from the workflow settings panel, complete with helpful hints about the {{project.wisdom}} feature.

The Results: More Than Just Smaller Prompts

The impact went beyond just solving our 374KB problem:

• 60%+ reduction in prompt sizes for our deep pipeline workflows
• Faster processing times due to smaller context windows
• Lower API costs from reduced token usage
• Smarter AI responses thanks to curated, relevant context instead of information overload
• Better project continuity as workflows can learn from previous work in the same project

Lessons Learned

What Worked Well

• Incremental approach: Building the digest system first, then adding project connections
• Smart defaults: Only compressing when it actually helps (>2KB threshold)
• Template flexibility: Supporting both .output and .digest gives users choice
• Type safety: All changes were fully typed and checked cleanly

Challenges We Faced

The biggest surprise? There weren't many major obstacles. The most significant issue was a pre-existing type error in an unrelated component (a Badge variant problem), which reminded us that sometimes the problems you expect aren't the ones that bite you.

The key was designing the system to be additive rather than disruptive—existing workflows continue to work exactly as before, while new ones can opt into the compression benefits.

What's Next?

With our context compression system in place, we're already seeing ideas for future enhancements:

• Adaptive compression: Automatically adjust digest detail based on downstream step requirements
• Cross-project learning: Let projects learn from patterns in related projects
• Compression analytics: Track which digest strategies work best for different workflow types

The Takeaway

Sometimes the best solutions come from stepping back and asking: "Do we really need all this information, or do we just need the right information?"

By building intelligent context compression into our AI workflow engine, we didn't just solve a technical problem—we made our system smarter, faster, and more cost-effective. And the best part? Our users get better results with less complexity.

If you're building AI-powered workflows and running into similar context cascade issues, consider implementing a digest system. Your prompts (and your API bills) will thank you.

Have you dealt with similar context explosion problems in your AI systems? I'd love to hear about your solutions in the comments below.