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deployment
Deploy Models with Ray Serve and Novita AI
Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.
Linked challenge: Agent for Auditable Financial Model Generation
Format
Code-aware
Lines
9
Sections
1
Linked challenge
Agent for Auditable Financial Model Generation
Prompt source
Original prompt text with formatting preserved for inspection.
9 lines
1 sections
No variables
1 code block
Outline a strategy for deploying the GPT-5 and Claude Sonnet 4 models used by your LlamaIndex agent using Ray Serve and Novita AI. Describe how Ray Serve would manage the inference endpoints for both models, ensuring scalability and reliability. Explain how Novita AI's capabilities could be integrated to optimize the inference runtime and cost for the financial analysis tasks. Provide conceptual code snippets for setting up a Ray Serve deployment. ```python
from ray import serve @serve.deployment
class GPT5Model: def __init__(self): # Initialize GPT-5 client pass async def __call__(self, text: str): # Call GPT-5 API return {"output": "..."} @serve.deployment
class ClaudeSonnet4Model: def __init__(self): # Initialize Claude Sonnet 4 client pass async def __call__(self, text: str): # Call Claude Sonnet 4 API return {"output": "..."} # serve.run(
# GPT5Model.bind(),
# ClaudeSonnet4Model.bind(),
# ) # Conceptual: LlamaIndex agent configuration to use Serve endpoints
# llm_gpt5 = OpenAI(model="hosted-gpt5", api_base="http://localhost:8000/gpt5")
```Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Preserve the source structure until you know which part of the prompt is actually driving the result quality.
Tune next
Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.
Verify after
Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.