DoCoreAI – AI Prompt Optimization Engine (Developer Edition)

Optimize LLM prompts • Tune temperature • Reduce LLM cost • Maximize OpenAI efficiency

DoCoreAI offers a streamlined, open‑source toolkit for prompt engineering and GPT optimization. Built for developers, this version includes core APIs and libraries —without SaaS features like dashboards. For the full experience (metrics, dashboards), explore our website.

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📊 See how much time, tokens & money you're saving with DoCoreAI's live insights dashboard

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🔬 What is DoCoreAI?

DoCoreAI is a research-first, open-source framework that optimizes large language model (LLM) responses on the fly — without retraining, fine-tuning, or prompt engineering.

It dynamically adjusts reasoning, creativity, precision, and temperature based on context and user role — so your AI agents respond with intelligence tailored to the task.

Whether you're building a support assistant, a creative co-pilot, or a data analyst bot — DoCoreAI ensures clear, cost-effective, and context-aware responses every time.

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🧩 DoCoreAI: Developer Edition vs SaaS Edition

Understand the difference between the open-source Developer Edition (available on GitHub) and the full-featured SaaS Edition (available at docoreai.com).

| Feature / Capability | Developer Edition (GitHub) | SaaS Edition (docoreai.com) | |---------------------------------------------------|----------------------------------------------------------------------|--------------------------------------------------------------------------------------------------| | Temperature Optimization | Demonstrates how temperature tuning works in code | Dynamically adjusts temperature between first and second LLM calls to reflect real impact | | Prompt Strategy | Uses self-reflection prompting to estimate ideal temperature | Same approach, but applies the estimated value in a second call for accurate optimization | | Dashboard & Reports | Not included | Includes dashboard with reports: Developer Time Saved, Token Waste, Cost Savings, etc. | | Target Users | Developers testing prompt behavior | Teams, product leads, and senior managers improving AI cost and efficiency | | Prompt Logging | Prompts not saved; used only in memory | Same — with an added feature to locally save prompts for developer inspection | | Role-Based Prompting | ✅ Supported | ✅ Supported |

💡 Both versions share the same base logic but differ in how deeply they optimize and visualize prompt performance.

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🌍 Why DoCoreAI?

❌ The Problem:

• LLMs respond generically, often missing the nuances of role-based intelligence.
• Manually tuning prompts or fine-tuning models is expensive, inconsistent, and doesn’t scale.
• Token usage grows unchecked, increasing operational costs.

✅ The DoCoreAI Solution:

• 🔁 Dynamic Intelligence Profiling: Adapts temperature, creativity, reasoning, and precision on-the-fly.
• 🧠 Context-Aware Prompt Optimization: Generates intelligent prompts for specific user roles or goals.
• 💸 Token Efficiency: Reduces bloat, avoids over-generation, and cuts down on API/token costs.
• 📦 Plug-and-Play: Use with OpenAI, Claude, Groq/Gemma, and other LLM providers.

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✨ Key Features

• intelligence_profiler() – Adjusts generation parameters intelligently per request
• token_profiler() – Audits cost, detects bloat, and suggests savings
• DoCoreAI Pulse – Test runner for benchmarking DoCoreAI against baselines
• Support for evaluating with MMLU, HumanEval, and synthetic prompt-response datasets

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📈 Milestones

• 🧪 10,000+ PyPI downloads within 40 days
• 🚀 Launched on Product Hunt
• 🧠 Active experiments: MMLU, HumanEval, Dahoas synthetic comparisons
• 📝 Reflection Blog: 25 Days of DoCoreAI

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🔬 DoCoreAI Lab – Research Vision

DoCoreAI Lab is an independent research initiative focused on dynamic prompt optimization, LLM evaluation, and token-efficiency in GenAI systems.

We believe that:

• AI responses can be smarter when intelligence is dynamically profiled instead of hardcoded via prompts.
• Evaluation should be real-time and role-aware, just like how humans adapt in different contexts.
• Token waste is solvable, and we’re on a mission to show how optimization can lower cost without compromising quality.

🔍 Current Focus Areas

• Dynamic temperature tuning based on role-context (Precision, Reasoning, Creativity)
• Cost profiling & token-efficiency evaluation:-> View Sheet (Work In Progress)
• Benchmarks (MMLU, HumanEval, Dahoas, etc.) to validate optimization methods
• Building toward a future product: DoCoreAI Pulse

🤝 We’re open to

• Collaborations with researchers, open-source contributors, and companies
• Exploratory conversations with incubators or AI investors

📬 Contact: email | LinkedIn

🗺️ Public Roadmap (Early View)

| Phase | Focus | |-------|-------| | ✅ Q1 2025 | Launched DoCoreAI on PyPI | | 🔄 Q2 2025 | Evaluation suite (DoCoreAI Pulse), token profiler, role-based tuning | | 🔜 Q3 2025 | Launch interactive web dashboard + SaaS preview | | 📣 Future | Open evaluation leaderboard, plugin ecosystem for agents |

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🚀 A New Era in AI Optimization

DoCoreAI redefines AI interactions by dynamically optimizing reasoning, creativity, and precision—bringing human-like cognitive intelligence to LLMs for smarter, cost-efficient responses.

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DoCoreAI simplified overview:

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🔥 Before vs. After DoCoreAI

| Scenario | ❌ Before DoCoreAI | ✅ After DoCoreAI | |---------------------|------------------|------------------| | Basic Query | "Summarize this report." | "Summarize this report with high precision (0.9), low creativity (0.2), and deep reasoning (0.8)." | | Customer Support AI | Responds generically, lacking empathy and clarity | Adjusts tone to be more empathetic and clear | | Data Analysis AI | Generic report with inconsistent accuracy | Ensures high precision and structured insights | | Creative Writing | Flat, uninspired responses | Boosts creativity and storytelling adaptability | | Token Efficiency | Wastes tokens with unnecessary verbosity | Optimizes response length, reducing costs |

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🔗 Step-by-Step Workflow:

1️⃣ User Query → A user submits a question/query to your application.
2️⃣ DoCoreAI Enhances Prompt → The system analyzes the query or prompt and generates an optimized prompt with dynamic intelligence parameters. The required intelligence range for each these parameters (like Reasoning - Determines logical depth, Creativity - Adjusts randomness , Precision - Controls specificity) are inferred from the query automatically.

3️⃣ Send to LLM → The refined prompt is sent to your preferred LLM (OpenAI, Anthropic, Cohere, etc.).
4️⃣ LLM Response → The model returns a highly optimized answer.
5️⃣ Final Output → Your application displays the AI’s enhanced response to the user.

👉 End Result? More accurate, contextually rich, and intelligent AI responses that feel human-like and insightful.

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💡 How DoCoreAI Helps AI Agents

DoCoreAI ensures that AI agents perform at their best by customizing intelligence settings per task. Here’s how:

📞 Support Agent AI → Needs high empathy, clarity, and logical reasoning.
📊 Data Analyst AI → Requires high precision and deep analytical reasoning.
🎨 Creative Writing AI → Boosts creativity for idea generation and storytelling.

This adaptive approach ensures that LLMs deliver role-specific, optimized responses every time.

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🚀 Use Cases: How DoCoreAI Enhances AI Agents across various domains

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