Prismatic

Your unified lens into reasoning, a powerful orchestration tool that lets you prompt, then instantly compare, score, and synthesize responses to get the best possible answer.

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Inspiration

What it does

PrismaticAI allows you to:

  • Prompt multiple LLMs (Claude, Grok, Gemini) simultaneously
  • View and compare their raw responses side-by-side
  • Automatically score outputs for clarity, correctness, and hallucination risk
  • Generate a fused, synthesized response using the best parts of each
  • Visualize model behavior with charts and analysis tools
  • Leverage the power of synthetic data and cross-model inference for prompt testing and benchmarking ## How I built it
  • Frontend: React + TypeScript, styled with TailwindCSS and ShadCN. Zustand for state management.
  • Backend: Node.js + Express handles prompt routing, API calls, scoring, and synthesis logic.
  • APIs:
    • Claude via Anthropic
    • Gemini via Google AI Studio
    • Grok via xAI’s OpenAI-compatible REST endpoint
  • Scoring and synthesis: Claude / Gemini (as a fallback) is used to analyze and merge model outputs.
  • UI/UX: Recharts for visual feedback, Framer Motion for transitions, and a clean split-pane layout for analysis.

Challenges I ran into

  • LLMs all require different prompt formats, auth headers, and error handling strategies.
  • Merging outputs into a single synthesized answer required careful prompting and tuning.
  • Accurately measuring correctness and hallucination potential across models is complex but essential.
  • Building a seamless multi-model interface under hackathon time constraints pushed prioritization and scope discipline. ## Accomplishments that I'm proud of

What I learned

  • The differences in LLM reasoning are subtle but meaningful—and fascinating to explore.
  • Prompt engineering is both an art and science—especially across models.
  • Synthesizing multiple outputs improves trust, clarity, and accuracy.
  • Measuring outputs for hallucination, truthfulness, and task fit is as important as generating them.
  • A great UX is essential when your product is working with nuanced, high-stakes content. ## What's next for Prismatic
  • 🔬 Add token usage and cost estimates per run
  • 🧠 Integrate deeper scoring for hallucination risk, factual consistency, and persona alignment
  • 🧪 Expand support for synthetic data generation and multi-model inference testing
  • 📘 Enable prompt versioning and session history
  • 🧰 Launch user accounts with shared workspaces, versioned prompt sets, and model testing campaigns
  • 👥 Build team collaboration features: see what your teammates are prompting, reviewing, or sharing
  • 🧩 Add more models (OpenRouter, Mistral, Perplexity, GPT-4)
  • 📊 Develop dashboards showing which LLMs perform best on different prompt types and personas
  • 🧱 Build a “prompting notebook” experience for researchers and analysts
  • 💳 Explore monetization via tiered usage plans or API proxying
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