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.
- DesignIntermediateNew
Design and Pitch an LLM-Powered Tutoring Product
As a 4-person team, deliver: (1) a product concept anchored in Jobs-to-be-Done (when X, I want Y so I can Z); (2) a Figma prototype of the full flow; (3) a partially functional …
- Product Design
- User Research
- LLM Evaluation
AI Software Engineering Group Project - PresentationBeginnerNew
Pitch an LLM Earnings-Call Analyst to an Equity Long-Short Team
Pick 3 publicly available US tech earnings-call transcripts (from a free source like sec.gov filings or company investor-relations pages) and build a retrieval-augmented LLM wor…
- Prompt Engineering
- Retrieval Augmented Generation
- LLM Evaluation
AI and Quantitative Finance - CodeIntermediateNew
Fine-Tune a Diffusion Model for an E-commerce Product Studio
You receive 1,200 curated product + lifestyle images across 6 product categories, a brand-style guide, and the company's current studio cost per image (around EUR 18). Fine-tune…
- Diffusion Models
- Stable Diffusion
- Dreambooth
Generative AI - StrategyBeginnerNew
Plan a Self-Improving Sales-Research Agent
Build the v0 agent: given a company URL, it gathers 5 fact bullets (recent news, headcount range, tech stack hints, hiring patterns, a recent leadership change) and drafts a 4-l…
- LLM Agents
- Agent Design
- Experimentation
AI Agents and LLM-Based Agents 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
- ResearchBeginnerNew
Run a Human-Preference Study Comparing Two Coding Assistants
Design a blinded paired-comparison study: 12 developer participants, each gets the same 8 realistic coding tasks (refactor, write a function, debug, test), each task is solved b…
- Experiment Design
- Statistical Evaluation
- Human Evaluation
AI Measurement and Evaluation - CodeIntermediateNew
Build an Evaluation Harness for an Internal LLM Assistant
You will design and implement an evaluation harness in Python that runs four test suites: (1) helpfulness (LLM-as-judge with rubric), (2) factual grounding (compare cited source…
- LLM Evaluation
- LLM As Judge
- Prompt Injection Testing
Large Language Models - CodeIntermediateNew
Fine-Tune a Diffusion Model for a Sustainable-Fashion Mood-Board Tool
You receive around 1,200 curated images of sustainable garments tagged with silhouette and material. Choose a base diffusion model (Stable Diffusion 1.5/2.1 or SDXL) and apply L…
- Diffusion Models
- Lora Fine Tuning
- Image Generation
Deep Generative Models - 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
Machine Translation - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- CodeIntermediateNew
Finetune a Diffusion Model for Sustainable-Fashion Mockups
You receive 1,200 product photos with paired captions and the brand's style guide. Fine-tune a Stable-Diffusion-class base model with LoRA (Low-Rank Adaptation, a parameter-effi…
- Diffusion Models
- Lora Finetuning
- Pytorch
Advanced Deep Learning - CodeIntermediateNew
Build a Vector-Search Backend for an Enterprise AI Knowledge Assistant
You receive a corpus of around 20,000 PDFs (mixed scanned and digital) totalling around 30 GB and a labeled retrieval set of 200 queries with human-judged ground-truth passages.…
- RAG
- Vector Search
- Embeddings
Data Engineering and Big Data Systems - 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
Ship an MVP RAG Knowledge Assistant for a Climate-Tech Startup
As a 4-person team across a 6-week sprint, ship: (1) an ingestion pipeline for around 4,000 mixed PDFs and markdown files; (2) a vector store with documented chunking strategy; …
- Retrieval Augmented Generation
- Software Engineering For Ai
- Vector Databases
AI Software Engineering Group Project 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
- ResearchIntermediateNew
Design a Capability Evaluation for an Open-Weights Coding Model
Pick a recent open-weights coding model (e.g., a Qwen, DeepSeek, or Llama variant). Design an evaluation set of around 40 coding tasks across 4 buckets: standard benign coding, …
- Capability Evaluation
- Safety Evaluation
- LLM Evaluation
AI Safety and Alignment - CodeIntermediateNew
LLM-Powered FAQ Chatbot for 40-Person SaaS Scale-up
You have access to TaskFlow's internal documentation, help articles, and a sample of 500 support tickets. Your task is to build a retrieval-augmented generation (RAG) pipeline: …
- LLM
- RAG
- Information Retrieval
Text Analytics and Natural Language Processing - CodeIntermediateNew
Multi-Agent Research Assistant for Biotech Patent Review
You receive 20 historical patent applications with the firm's own prior-art memos as ground truth. Design and build a 3-agent system: (a) Searcher — issues queries to a patent-s…
- LLM Agents
- Multi Agent Collaboration
- Agent Evaluation
AI Agents and LLM-Based Agents - AnalysisIntermediateNew
Cut Latency and Cost on a High-Volume Summarization Service
You receive 30 days of anonymized request logs (prompt token counts, completion token counts, latencies, models used). Profile the cost and latency distribution, then design and…
- Cost Optimization
- Latency Optimization
- Prompt Compression
LLM Application Development - CodeIntermediateNew
Design a Visual Search Backend for a Boutique Luxury Marketplace
You receive a catalog of 80,000 luxury items (image + sparse metadata) and a labeled query set of 300 user photos with hand-picked target items. Choose an embedding strategy (CL…
- Visual Search
- Embeddings
- Clip
Deep Learning for Computer Vision - 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
LLM Application Development - CodeIntermediateNew
AI-Driven Sales Lead Scoring for a B2B SaaS Scale-Up
You will receive a sample dataset of 200 leads with fields like company size, industry, email open rates, and website visits. Using AI tools, you must craft prompts to generate …
- Prompt Engineering
- Lead Scoring
- Data Analysis
Data-Driven Prototyping with AI - ResearchIntermediateNew
QLoRA Fine-Tune for a Customer-Support Domain Assistant
You receive 8,000 anonymized support ticket pairs (question -> agent response), the company's product documentation (around 600 pages), and a strong RAG baseline already running…
- Qlora
- Fine Tuning
- RAG
Fine-Tuning Large Language Models - 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
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - ResearchIntermediateNew
Red-Team a Customer-Service Chatbot for Jailbreak Resistance
Use a published taxonomy of jailbreak categories (prompt injection, persona override, encoded payloads, multi-turn escalation, refusal bypass, tool-misuse). For each category, d…
- Red Teaming
- Jailbreak Analysis
- LLM Evaluation
AI Safety and Alignment - 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
Neural Networks for NLP - CodeIntermediateNew
Build a LangGraph Multi-Agent Researcher
Design the four-agent topology with explicit message contracts. Implement each agent as a separate LLM call with role-specific system prompts, tool access (web search for resear…
- Multi Agent Orchestration
- Langgraph
- LLM Tool Use
Multi-Agent Systems
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.
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