Implement Hybrid Reasoning for Information Gathering

Prompt detail, context, and execution controls for real reuse instead of one-off copying.

implementationAdvanced Chip R&D and CI with Gemini Pro and CrewAIPublic prompt

Operator-ready prompt for reuse, tuning, and workspace runs.

This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.

Best for

Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.

Reuse pattern

Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.

Before first run

Swap domain facts, examples, and any hard-coded entities for your own context.

Tighten the evidence or verification requirement if this is headed toward production.

Decide which failure mode you want to evaluate first before you branch the prompt.

Operator lens

This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.

Best practice: keep one pristine source version, then branch variants around evaluation criteria, evidence thresholds, and output format.
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Run Profile

Open this prompt inside Workspace when you want a live iteration loop.

Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.

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Prompt content

Original prompt text with formatting preserved for inspection and clean copy.

Source prompt
1 active lines
1 sections
No variables
0 checklist items
Raw prompt
Formatting preserved for direct reuse
Design how your 'Materials Science Researcher' and 'Patent Analyst' agents will utilize hybrid reasoning. The 'Patent Analyst' should use Claude Sonnet 4 for quick patent abstract summarization (instant reasoning), while the 'Materials Science Researcher' should use Gemini 2.5 Pro (Deep Think) for detailed analysis of scientific papers (deep reasoning) retrieved via RAG. Detail the RAG integration with scientific/patent databases.

Adaptation plan

Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Safe workflow

Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.

Prompt diagnostics

Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.

Sections
1
Variables
0
Lists
0
Code blocks
0
Reuse posture

This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.

Linked challenge

Advanced Chip R&D and CI with Gemini Pro and CrewAI

Inspired by Substrate's ambitious efforts in particle acceleration lithography to challenge chip manufacturing giants like TSMC and ASML, this challenge focuses on building an advanced multi-agent system for accelerated R&D and competitive intelligence (CI) in the semiconductor industry. Participants will orchestrate a team of specialized agents using CrewAI, leveraging the 'Deep Think' capabilities of Gemini 2.5 Pro for scientific discovery and Claude Sonnet 4 for rapid market analysis. This system will perform in-depth scientific literature reviews, analyze patent landscapes, assess competitor strategies, and generate novel research hypotheses for cutting-edge lithography techniques. It will implement a hybrid reasoning approach, combining instant insights with deep scientific inquiry, and utilize adaptive thinking budgets to optimize resource allocation for complex materials science problems.

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Prompt origin
Why open it

Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.

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