Green Hydrogen Feasibility Assessment System using AI SDK and Llama 3.3 70B
Masdar's shelving of Abu Dhabi green hydrogen projects highlights shifting economic viability thresholds. Build a TypeScript agent using Vercel AI SDK powered by Llama 3.3 70B model inference to evaluate levelized cost of hydrogen (LCOH) sensitivity under changing power and capital cost inputs.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an AI SDK workflow integrating Llama 3.3 70B to simulate economic feasibility and shelving risk for GCC green hydrogen investments.
How work is evaluated
Evaluates LCOH calculation precision and project shelving classification logic based on threshold economics.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Shelving Status Recommendation Match
Ensures agent correctly flags unviable projects as SHELVE.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
LCOH Accuracy Error
Absolute error between target LCOH and predicted LCOH. • target: 0.05 • range: 0-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Orchestrate financial analysis workflows using Vercel AI SDK and Llama 3.3 70B
Implement mathematical tools for Levelized Cost of Hydrogen (LCOH) calculation
Formulate decision trees to flag project pause or termination risks based on market thresholds
Stream comprehensive executive investment memos to client frontend interfaces
Reference links and supporting material
Financial and operational parameters for green hydrogen projects across the GCC, reflecting power pricing and electrolyzer capital expenditure models.
Challenge setup
Evaluates LCOH calculation precision and project shelving classification logic based on threshold economics.
Challenge input
JSON object with power_cost_kwh_usd, electrolyzer_capex_kw_usd, and off_take_price_kg_usd.
AI SDK
Unified TypeScript model interaction and tool execution
Llama 3.3 70B
High performance open model for complex financial analysis
Evaluated output
JSON object with calculated_lcoh_usd, status_recommendation, and margin_usd.
- Ensures agent correctly flags unviable projects as SHELVE.
- Absolute error between target LCOH and predicted LCOH. • target: 0.05 • range: 0-5
- Benchmark: GCC Renewable Energy Financial Benchmark
- LCOH Accuracy Error target: 0.05
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Llama 3.3 70B · Required
- Zed · Optional
- Vercel AI SDK · Optional
- Vercel AI SDK · Optional
Evaluation contract
- Shelving Status Recommendation Match · Weight 1
- LCOH Accuracy Error · Weight 1
Metrics
- LCOH Accuracy Error · Target 0.05
Benchmarks
- GCC Renewable Energy Financial Benchmark
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with power_cost_kwh_usd, electrolyzer_capex_kw_usd, and off_take_price_kg_usd.
Output: JSON object with calculated_lcoh_usd, status_recommendation, and margin_usd.
Execution harness: JavaScript
Evaluation tools
- AI SDK · Agent Framework
- Llama 3.3 70B · Model Runtime
Recipe freeze marker
No recipe freeze time is recorded.