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.
- 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
Open coursework - DesignSeniorNew
Design Eval Suite for a Multimodal Brainstorming Assistant
You receive (1) the assistant's current API, (2) a list of 6 launch user-personas, and (3) the product team's quality target ('beat the previous model on 4 of 6 personas'). Desi…
- LLM Evaluation
- Multimodal Evaluation
- Safety Evaluation
Generative AI - CodeBeginnerNew
Prototype a Multimodal Visual-Question-Answering Demo
You will use a small open-source vision-language model (e.g., LLaVA-1.5-7B or PaliGemma) and prompt-engineer it for the warehouse-VQA task. Build a Gradio web demo. Construct a …
- Vision Language Models
- Multimodal Perception
- Prompt Engineering
Open coursework - DesignIntermediateNew
Spec Trust-and-Safety Eval Harness for an LLM-Powered Customer-Support Bot
You will spec a 6-page evaluation harness covering: (1) jailbreak test set (about 200 prompts across 6 attack families), (2) PII-leakage probes (about 100 synthetic-customer pro…
- LLM Evaluation
- Red Teaming
- Pii Detection
Open coursework 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
- DesignBeginnerNew
Build the PRD for an Internal RAG Knowledge Assistant
You receive: a description of the CS workflows (post-sale onboarding, escalation, renewal), an inventory of internal knowledge sources (Notion, Salesforce, Zendesk macros, 3 pro…
- Product Management
- Retrieval Augmented Generation
- Evaluation Design
AI for Business and AI Product Management - ResearchIntermediateNew
Audit a Public LLM Benchmark for Validity Threats
Choose one open LLM benchmark (e.g., MMLU, GPQA, BIG-Bench-Hard, MATH). Read the benchmark paper plus at least three follow-up critiques. Audit (1) data contamination risk again…
- Benchmark Evaluation
- Data Contamination Analysis
- Annotation Methodology
AI Measurement and Evaluation - 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
Open coursework - CodeIntermediateNew
Natural Language Inference for an HR-AI Compliance Tool
Use SNLI/MNLI/ANLI as starting data and curate 200 domain-specific HR examples (synthetic or anonymized) for fine-tuning. Fine-tune a small encoder (DeBERTa-v3-base or similar),…
- Natural Language Inference
- Transformer Models
- Fine Tuning
Open coursework - 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
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 - 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 - CodeBeginnerNew
Build Semantic Search for an Internal Engineering Wiki
You receive a Confluence XML export (~12k pages, ~80 MB of text) and a hand-labeled benchmark of 50 internal queries with ground-truth doc IDs. Chunk and embed the corpus with a…
- Embedding Models
- Vector Search
- Pgvector
Vector Databases and Embeddings - CodeIntermediateNew
Design Prompt Versioning and Observability for a Coding Assistant
You will (1) design a prompt-registry data model (versions, owners, environments, change log) and implement it in Postgres + a small Python SDK, (2) instrument the assistant to …
- Prompt Versioning
- Observability
- Pii Scrubbing
LLM Application Development 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
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 - 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
Instruction-Tune a Small Model for an Edtech Tutor
You receive a 1.5B base model (e.g., SmolLM-1.7B or Qwen-1.8B), permission to use 2 hours of a rented A100, and a curated seed of around 5,000 math-tutoring dialogues. Augment w…
- Instruction Tuning
- Supervised Fine Tuning
- Dataset Curation
Open coursework - ResearchSeniorNew
Multi-Tenant Vector Isolation for a B2B Knowledge Assistant
Build a small proof-of-concept in your chosen vector store (Pinecone or Qdrant — pick one and justify) that supports 10 simulated tenants with 1,000 vectors each. Implement the …
- Multi Tenant Isolation
- Vector Databases
- Threat Modeling
Open coursework - 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 - 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
Open coursework - CodeIntermediateNew
Fine-Tune ASR for a Healthcare Voice-Note Startup
You receive about 40 hours of de-identified clinician voice notes paired with corrected transcripts plus a medical-terminology lexicon (about 8,000 drug + procedure terms). Fine…
- Asr
- Speech Recognition
- Domain Adaptation
Speech Recognition and Spoken Language Processing - 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
Open coursework - 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
Open coursework - CodeIntermediateNew
Fine-Tune a Sequence-to-Sequence Model for Code-Doc Generation
Take a small base model (CodeT5+ or a distilled CodeLlama-Instruct). Build the dataset by mining around 8,000 high-quality function-docstring pairs from permissively-licensed Py…
- Seq2seq
- Transformers
- Lora Fine Tuning
Neural Networks for NLP - 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 - 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
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
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.



















































































