AI & Data
Generative AI & LLMs Challenges
Generative AI & LLMs challenges put you inside the work of building with large language models. You'll develop skills in prompt patterns, few-shot prompting, chain-of-thought, and LLM API integration, learning how these models behave before you scale them.
From there you'll handle the harder edges — RAG architectures, vector database basics, fine-tuning, and prompt versioning — putting LLM guardrails and LLM evaluation around every deployment the way AI teams actually do. Each challenge you solve earns a verified credential you can share with recruiters.
- AnalysisBeginnerNew
Run an A/B Test on Two System Prompts for a Sales Email Assistant
You will (1) design the A/B test (random assignment by rep_id, 50/50 split, 2-week duration), (2) instrument three primary metrics: reply rate (event-based), average tokens per …
- Prompt Evaluation
- Ab Testing
- Metric Design
Open coursework - DesignBeginnerNew
Design an Automated Essay-Feedback System
You receive 20 anonymized middle-school essays scored by 2 human teachers on a 4-dimension rubric (structure, evidence, voice, mechanics). Design an LLM-based feedback system th…
- Automated Assessment
- Rubric Design
- Prompt Engineering
AI in Education and Learning Analytics - CodeIntermediateNew
Wire a Knowledge Graph into a Pharma RAG Assistant
You receive: 100 internal benchmark questions with reference answers; a 50,000-document anonymized RAG index; a curated drug-target-disease KG (~80,000 triples) loaded into a tr…
- Kg Grounded RAG
- Sparql
- Entity Linking
Open coursework - DesignBeginnerNew
Generative AI Content Strategy for a Sustainable Fashion Brand
You must first define EcoWeave's brand voice by analyzing their existing content (provided). Then, design a set of prompts and a workflow (e.g., using ChatGPT or a no-code AI to…
- Prompt Engineering
- Content Strategy
- Brand Voice
Data-Driven Prototyping with AI Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
Build a Tool-Calling Agent for an Internal Reporting Bot
You will implement the agent in either LangChain or LlamaIndex (your choice; defend it in the readme). Wire 4 tools: (1) read-only SQL on a sample warehouse, (2) a mocked BI met…
- Agent Orchestration
- Tool Calling
- Langchain Or Llamaindex
Open coursework - DesignBeginnerNew
Chain-of-Thought for High-School Math Tutoring
You receive 80 practice problems across 4 topics (linear equations, factoring, systems of equations, quadratics), each with the correct answer and an expected age-appropriate ex…
- Chain Of Thought Prompting
- Zero Shot Prompting
- Few Shot Prompting
Open coursework - AnalysisBeginnerNew
Cost-Optimize an Embedding Pipeline for a Customer Support Knowledge Base
You receive: (a) the current pipeline (full re-embed on any article change, OpenAI text-embedding-3-large, 3,072 dims) with one month of cost logs, (b) a sample of 5,000 article…
- Embedding Models
- Cost Optimization
- Change Detection
Open coursework - DesignIntermediateNew
Build Hybrid Keyword-Plus-Embeddings Search for a Legal Document Portal
Using the product context brief (legal_portal_brief), design a hybrid retrieval system that combines BM25 keyword scoring with dense embeddings and fuses the two ranked lists wi…
- Information Retrieval
- Bm25
- Vector Search
Open coursework - Browse challenges
Explore role
Marketing Analyst
Plan and measure campaigns that grow the business. Funnel analytics, attribution, segmentation, and the rigorous measurement that lets marketing defend its budget at the leadership table.
- ResearchIntermediateNew
Fine-Tune a Vision-Language Model for Image Captioning
Take BLIP-2 or LLaVA-1.6 as the base. Fine-tune (LoRA is fine) on a 4,000-image accessibility-curated dataset where each image has a useful caption written by a low-vision-exper…
- Vision Language Models
- Lora Fine Tuning
- Pytorch
Multimodal Machine Learning - CodeIntermediateNew
Build an Internal-Tools Agent for a Mid-Cap Enterprise
You receive OpenAPI specs for 4 mock internal APIs and 30 reference question-answer pairs spanning easy lookups and multi-tool chains. Build the agent using an LLM tool-use fram…
- LLM Agents
- Tool Use
- Agent Evaluation
AI Agents and LLM-Based Agents - CodeIntermediateNew
Build a Domain Instruction-Tuning Recipe for a Legal Coach
You will source instruction data from three streams: ~3,000 synthetic paralegal Q&A generated by a frontier model (anonymized prompts), ~1,500 curated examples from public legal…
- Instruction Tuning
- Lora Fine Tuning
- Data Curation
Open coursework - ResearchSeniorNew
Plan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab
You will produce (1) a 6-page survey of four PEFT methods (LoRA, adapters, prefix tuning, IA3) with their strengths, weaknesses, and parameter footprints, (2) a one-page decisio…
- Parameter Efficient Fine Tuning
- Transfer Learning
- Fine Tuning
Open coursework Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- CodeIntermediateNew
Fine-Tune a Transformer for Customer-Support Triage at an Enterprise AI Vendor
You receive 240,000 labeled support tickets across 14 queues, with English, Bahasa Indonesia, and Tagalog. Fine-tune a multilingual transformer encoder (XLM-RoBERTa-base is a st…
- Transformers
- Fine Tuning
- Multilingual NLP
Deep Learning - CodeIntermediateNew
Build a Cross-Lingual Retrieval-Augmented QA System
Index around 5,000 internal-knowledge docs across the three languages using a multilingual embedding model (e.g., multilingual-e5 or BGE-M3). Build the retrieval-then-answer pip…
- RAG
- Cross Lingual Retrieval
- Multilingual Embeddings
Open coursework - CodeIntermediateNew
Train a Domain-Specific Reranker for a Legal-Tech Search Box
You receive 20,000 (query, document, relevance-label) triples from the firm's contract corpus. Fine-tune a small cross-encoder (e.g., ms-marco-MiniLM-L-6-v2 or BAAI/bge-reranker…
- Cross Encoder Reranker
- Fine Tuning
- Ir Evaluation
Open coursework - CodeIntermediateNew
LoRA Fine-Tune a 7B LLM for Legal-Clause Extraction
You receive a curated extraction dataset (2,000 train, 500 val, 500 test contracts with span-level labels across 12 clause types) and a fine-tunable 7B base model (e.g., Llama-3…
- Lora
- Fine Tuning
- Parameter Efficient Tuning
Open coursework - ResearchIntermediateNew
Neuro-Symbolic Question Answering on an Enterprise Knowledge Graph
You receive a curated Turtle-format knowledge graph (around 2 million triples covering organizational structure, products, projects), 200 labeled question-SPARQL pairs split 140…
- Neuro Symbolic
- Sparql
- Knowledge Graphs
Open coursework - CodeIntermediateNew
Fine-Tune a Small Transformer for Legal-Domain EN-DE Translation
You receive a 120,000-segment parallel EN-DE legal corpus and a held-out 1,000-segment test set with reference translations. Fine-tune a small pretrained Transformer (e.g., NLLB…
- Neural Mt
- Transformer
- Fine Tuning
Open coursework
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
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