implementation

Knowledge Base Construction

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Design Solid-State Liquid-Like Conductors

Format

Text-first

Lines

Sections

Linked challenge

Design Solid-State Liquid-Like Conductors

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Initialize a ChromaDB collection and implement a script to ingest chemical abstracts. Focus on metadata tagging for 'phase' (solid/liquid) and 'conductivity' values.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next 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.

Prompt diagnostics

Variables

Lists

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Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Design Solid-State Liquid-Like Conductors

Inspired by the recent discovery of organic molecules that maintain liquid-like ion conductivity within a solid lattice, this challenge task you with building a sophisticated discovery pipeline. You will utilize DeepSeek-R1, a reasoning-focused LLM, to hypothesize structural motifs that enable high cation mobility in crystalline states. By leveraging DSPy for programmatic prompt optimization, you will refine the model's ability to extract specific molecular descriptors from chemical literature stored in a ChromaDB vector database. The goal is to automate the identification of candidate electrolytes that bridge the gap between high-safety solid-state batteries and high-performance liquid systems. You will implement a Retrieval-Augmented Generation (RAG) system that doesn't just find text, but reason over chemical constraints such as lattice energy, packing density, and ionic radius to suggest novel derivatives of the discovered molecules.

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