Multi-agentTechnology & Data AnalysisAdvancedFree

AI Model Selection & Deployment System.

3 agents, 3 deliverables.

Run a multi-agent workflow to produce an execution-ready ai model selection & deployment system deliverable.

WORKFLOW META

Agents3

Total tokens (avg)~500

Run time9 min

AI toolChatGPT · Claude · Gemini

Variables4

DifficultyAdvanced

CategoryTechnology & Data Analysis

SEQUENCE MAP · CLICK TO JUMP

· 01 ·

AI Strategy & Model Selection Agent

· 02 ·

Prompt Engineering & RAG System Designer

· 03 ·

AI Integration & API System Builder

USE CASE INPUTS

Set the workflow's inputs once.

These variables feed into every agent prompt below. Fill them once, then copy each agent in order.

Use case for ai

AI Model Selection & Deployment System example context

Technical maturity

AI Model Selection & Deployment System example context

Budget

Rs 50,000

Data availability

website analytics export and top-performing pages

THE AGENTS

The 3-step sequence.

AGENT · AI STRATEGY

AI Strategy & Model Selection Agent

GOAL OF THIS STEP

Design the AI strategy. Define: the AI approach best suited to this use case and team (API-based LLM / open-source model / fine-tuned model / custom ML model — rank by: fit, cost, required expertise), the model candidates for the top approach (e.g., for LLM API: GPT-4o vs. Claude vs. Gemini vs. Llama — compare on cost per token, context window, capability for use case, rate limits), and the build vs. buy vs. fine-tune decision framework. Recommend the best starting point and the path to upgrade as needs grow.

EXPECTED OUTPUT

AI approach recommendation + model comparison table + build/buy/fine-tune decision + starting point + upgrade path

agent-01-ai-strategy-&-model-selection-.md

### Input
Use case, maturity, budget, data

### Task
Design the AI strategy. Define: the AI approach best suited to this use case and team (API-based LLM / open-source model / fine-tuned model / custom ML model — rank by: fit, cost, required expertise), the model candidates for the top approach (e.g., for LLM API: GPT-4o vs. Claude vs. Gemini vs. Llama — compare on cost per token, context window, capability for use case, rate limits), and the build vs. buy vs. fine-tune decision framework. Recommend the best starting point and the path to upgrade as needs grow.

### Output
AI approach recommendation + model comparison table + build/buy/fine-tune decision + starting point + upgrade path

AGENT · PROMPT ENGINEERING

Prompt Engineering & RAG System Designer

AGENT · AI INTEGRATION

AI Integration & API System Builder

HOW TO RUN

Three steps. 9 min.

STEP 01

Fill in the variables at the top. Copy them into a note or your tool's context window — every agent below uses them.

STEP 02

In your AI tool, paste Agent 1 and run it. Copy the output. Paste Agent 2 with the output appended. Repeat in order for all 3 agents.

STEP 03

At the final agent, review and refine. It outputs your finished deliverable, ready to publish or hand off.

WHAT YOU GET

The final output, end-to-end.

AI Strategy & Model Selection Agent

AI approach recommendation + model comparison table + build/buy/fine-tune decision + starting point + upgrade path

Prompt Engineering & RAG System Designer

System prompt structure + few-shot strategy + CoT approach + output format + RAG system design + prompt testing framework

AI Integration & API System Builder

Integration plan at appropriate technical level + rate limiting + cost controls + fallback strategy + monthly cost calculator

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The Multi-Agent Operator Pack.

100 production-ready workflows like this one. Agent prompts, variable cheat-sheets, and the operator's guide.

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AI Model Selection & Deployment System.

Set the workflow's inputs once.

The 3-step sequence.

AI Strategy & Model Selection Agent

Prompt Engineering & RAG System Designer

AI Integration & API System Builder

Three steps. 9 min.

The final output, end-to-end.

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