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.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same 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.
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.
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.
Structured source with 1 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Create an Akash SDL file to deploy a Llama-3-8B-Instruct model using an Ollama container. Ensure the service is exposed on a public port and provide the Python code to initialize a LangChain ChatOllama instance pointing to this provider's IP.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the source structure until you know which part of the prompt is actually driving the result quality.
Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.
Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.
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.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Decentralized Tech-Watch RAG System using LangChain and Akash Network
The IEEE RAS Science and Technology Watch Board requires sophisticated tools to track emerging trends in robotics and automation. In this challenge, you will build a Retrieval-Augmented Generation (RAG) system that monitors technical publications and identifies 'High Impact' breakthroughs. You will use LangChain (and LangGraph for workflow state) to build the pipeline, but the heavy lifting of model inference will be hosted on the Akash Network—a decentralized cloud. This ensures that the technical watch system is resilient, cost-effective, and not reliant on a single centralized provider. You will implement a custom LangChain LLM class that interfaces with a model (like Llama 3) deployed on Akash providers.
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.