15 AI & LLM Projects (2026)

In this tutorial, you'll learn about 15 ai & llm projects (2026). We cover key concepts, practical examples, and best practices to help you understand and apply this topic effectively.

Large language models are reshaping how we build software. These 15 projects teach you prompt engineering, retrieval-augmented generation, AI agent architectures, and fine-tuning — skills that are in high demand. Start with simple API wrappers and progress to custom RAG pipelines and autonomous agents.

Beginner Projects

1. Prompt Engineering Playground

Difficulty: ⭐
Skills: LLM API (OpenAI/Claude), prompt design, temperature/top-p tuning
Build a UI for testing prompts. Features: adjustable system/user prompts, temperature slider, response streaming, save prompt templates, compare responses side by side.

2. AI Chatbot UI

Difficulty: ⭐
Skills: Chat completion API, conversation history, streaming responses
Build a clean chat interface for any LLM. Features: message bubbles, markdown rendering in responses, chat history persistence, stop generation button, dark mode.

3. Markdown-to-Text Summarizer

Difficulty: ⭐
Skills: LLM summarization, chunking long text, token counting
Build a tool that summarizes markdown documents. Features: paste markdown input, configurable summary length (short/medium/long), bullet or paragraph output, export summary.

4. AI Email Reply Generator

Difficulty: ⭐⭐
Skills: Prompt templates, context injection, tone control
Build an assistant that drafts email replies. Features: paste received email, select tone (formal/friendly/urgent), generate reply draft, edit and copy, multiple variations.

5. Content Rewriting Tool

Difficulty: ⭐⭐
Skills: Paraphrasing, style transfer, prompt chaining
Build a tool that rewrites content in different styles. Features: rewrite as professional/casual/academic, preserve key facts, length control (shorter/longer), batch processing for multiple paragraphs.

Intermediate Projects

6. RAG Pipeline (PDF Q&A)

Difficulty: ⭐⭐⭐
Skills: Document chunking, embeddings, vector DB (Chroma/Pinecone), retrieval
Build a system that answers questions from PDF documents. Features: PDF ingestion and chunking, embedding generation, vector store indexing, semantic search, answer generation with source citations.

7. AI Research Assistant (Web Search + LLM)

Difficulty: ⭐⭐⭐
Skills: Web search API integration, result summarization, citation
Build a research tool that searches the web and summarizes findings. Features: query multiple sources, extract relevant snippets, generate research summary, cite sources, export to markdown.

8. Custom Chatbot with Memory

Difficulty: ⭐⭐⭐
Skills: Conversation buffer, session management, summarization memory
Build a chatbot that remembers past conversations. Features: short-term (recent messages) and long-term (summarized) memory, user identification, memory retrieval on relevant topics, forget/reset command.

9. AI Code Review Tool

Difficulty: ⭐⭐⭐
Skills: Code context injection, diff analysis, best practices prompting
Build a tool that reviews code diffs. Features: paste code or diff, auto-detect language, review categories (bugs, style, security, performance), suggestion generation, pass/fail rating.

10. Meeting Note Taker (Transcription + Summary)

Difficulty: ⭐⭐⭐⭐
Skills: Speech-to-text API, LLM summarization, speaker diarization
Build a tool that transcribes and summarizes meetings. Features: upload audio file, speaker identification, timestamped transcript, action item extraction, meeting summary with key decisions.

11. Multi-Agent Research System

Difficulty: ⭐⭐⭐⭐
Skills: Agent Orchestration, task delegation, tool use
Build a system with multiple AI agents that collaborate. Features: orchestrator agent delegates to specialist agents (search, summarize, fact-check), agents use tools (web, calculator, DB), final synthesized report.

Advanced Projects

12. Fine-Tune a Small LLM (LoRA)

Difficulty: ⭐⭐⭐⭐⭐
Skills: LoRA / QLoRA, Hugging Face transformers, dataset preparation, evaluation
Fine-tune a small open-source LLM (Llama 3, Mistral, Phi-3) on custom data. Features: prepare instruction dataset, LoRA config, training with PEFT, inference with merged weights, evaluate on holdout set.

13. AI Coding Agent

Difficulty: ⭐⭐⭐⭐⭐
Skills: Code Generation, sandboxed execution, iterative debugging
Build an agent that writes and tests code. Features: natural language task input, generate code skeleton, execute in sandbox, read errors and fix, test generation, explain code output.

14. Autonomous Web Research Agent

Difficulty: ⭐⭐⭐⭐⭐
Skills: Browser automation (Playwright/Selenium), planning, reflection
Build an agent that autonomously researches a topic. Features: accept research question, plan sub-questions, browse websites, extract relevant content, synthesize findings, cite sources, produce structured report.

15. LLM Evaluation Harness

Difficulty: ⭐⭐⭐⭐
Skills: Benchmark datasets, metrics calculation, model comparison
Build a system to evaluate LLM performance. Features: load benchmark datasets (MMLU, TruthfulQA, GSM8K), run evaluations across multiple models, accuracy/F1/ROUGE scores, leaderboard visualization, regression detection.

16. AI Document Analysis Pipeline

Difficulty: ⭐⭐⭐⭐
Skills: Multi-modal LLMs, OCR, document parsing, structured extraction
Build a pipeline that analyzes scanned documents. Features: OCR text extraction, classify document type (invoice, contract, report), extract structured fields (dates, amounts, parties), validate extracted data, export to JSON.

17. Custom RAG with Hybrid Search

Difficulty: ⭐⭐⭐⭐⭐
Skills: Dense + sparse retrieval (BM25), re-ranking, query expansion
Build an advanced RAG system with hybrid search. Features: dense embeddings + BM25 keyword search, reciprocal rank fusion, cross-encoder re-ranking, query expansion with LLM, ablation study to compare retrieval methods.

18. LLM-Powered Data Extraction Tool

Difficulty: ⭐⭐⭐⭐
Skills: Structured output (JSON mode), schema definition, batch processing
Build a tool that extracts structured data from unstructured text. Features: define extraction schema (JSON), process batch of documents, validate extracted data, confidence scoring, handle extraction failures with fallback.

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