Applied AI for Software Developers Certification in Oman

**Install:** VS Code LLM plugins, Postman, LangChain/Transformers libraries

**Setup:** API keys (OpenAI, Hugging Face, Gemini)

**Test:** Minimal “Hello LLM” prompt-response from terminal & playground

**Hands-on:** Create a CLI tool that consumes an LLM prompt via API

Real-world architecture patterns:

Prompt → API → UX, Prompt → VectorDB → Context → Output

Understanding Model Capabilities: Reasoning, Creativity, Memory

Responsible AI: Model trustworthiness, hallucination handling

RESTful APIs: OpenAI, Gemini, Cohere, Claude

Rate limits, token management, API costs

Prompt composition in code (Python/JS)

Response parsing and retry logic

Streaming vs synchronous models

Prompt templates, chaining, few-shot prompting

Zero-shot vs prompt tuning vs fine-tuning: When to use what

Prompt structure: Inputs, Instructions, Examples, Format hints

Output validation using regex/schema (Pydantic)

LangChain: Chains, Agents, Memory

LlamaIndex: Document loaders, indexes, query engines

Tool/agent orchestration vs simplicity tradeoffs

Connecting data: PDFs, SQL, Web Pages

Embeddings explained: cosine similarity & search

Tools: OpenAI embeddings, Hugging Face, Instructor

Vector DBs: ChromaDB, Pinecone, Weaviate – which one and why?

Chunking strategies, metadata filters, scoring strategies

Evaluation: Recall, relevancy, hallucination control

Frontend-first vs API-first vs CLI-first strategies

Streamlit: Fast UIs for internal demos

Gradio: Interactive models for PoCs

FastAPI: Secure, scalable, testable endpoints

Bonus: Dockerization + CI/CD tips for AI apps

LoRA, PEFT, QLoRA – tuning without burning your GPU

Hugging Face: Model hub, datasets, trainers

Fine-tune vs. Retrieval-Augmented vs. Prompt Templates

Budgeting: Compute cost vs performance gain

Deployment options: AWS, Hugging Face Inference, GCP

Integrating LLMs in React apps using OpenAI SDK

Flutter AI workflows: Dart + HTTP + Cloud APIs

Prompt UX patterns: dynamic autofill, summarizers, assistants

Edge computing for AI (e.g., Ollama, Mistral on device)

Token budgeting: Prompt cost per feature pattern

Monitoring LLM APIs: Logs, quality metrics, latency

Bias, hallucination, and harmful response filters

Model comparison (GPT-4 vs Claude vs Cohere vs Mistral)

Cost control patterns: Dynamic model fallback, temperature tuning

Resume Matcher: RAG-based resume-to-JD matcher

Code Annotator: JS plugin for live code explanations

AI Doc Assistant: Open-source markdown doc Q&A bot

Dev CLI Agent: Chat-style AI CLI command generator

Copy Generator: AI for personalized product copywriting

Enable CI/CD for AI pipelines, add A/B prompt testing, or deploy via Hugging Face Spaces