Applied AI for Software Developers Certification in Qatar

Upcoming sessions

Curriculum

1

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

2

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

3

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

4

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

1

Real-world architecture patterns:

2

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

3

Understanding Model Capabilities: Reasoning, Creativity, Memory

4

Responsible AI: Model trustworthiness, hallucination handling

1

RESTful APIs: OpenAI, Gemini, Cohere, Claude

2

Rate limits, token management, API costs

3

Prompt composition in code (Python/JS)

4

Response parsing and retry logic

5

Streaming vs synchronous models

1

Prompt templates, chaining, few-shot prompting

2

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

3

Prompt structure: Inputs, Instructions, Examples, Format hints

4

Output validation using regex/schema (Pydantic)

1

LangChain: Chains, Agents, Memory

2

LlamaIndex: Document loaders, indexes, query engines

3

Tool/agent orchestration vs simplicity tradeoffs

4

Connecting data: PDFs, SQL, Web Pages

1

Embeddings explained: cosine similarity & search

2

Tools: OpenAI embeddings, Hugging Face, Instructor

3

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

4

Chunking strategies, metadata filters, scoring strategies

5

Evaluation: Recall, relevancy, hallucination control

1

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

2

Streamlit: Fast UIs for internal demos

3

Gradio: Interactive models for PoCs

4

FastAPI: Secure, scalable, testable endpoints

5

Bonus: Dockerization + CI/CD tips for AI apps

1

LoRA, PEFT, QLoRA – tuning without burning your GPU

2

Hugging Face: Model hub, datasets, trainers

3

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

4

Budgeting: Compute cost vs performance gain

5

Deployment options: AWS, Hugging Face Inference, GCP

1

Integrating LLMs in React apps using OpenAI SDK

2

Flutter AI workflows: Dart + HTTP + Cloud APIs

3

Prompt UX patterns: dynamic autofill, summarizers, assistants

4

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

1

Token budgeting: Prompt cost per feature pattern

2

Monitoring LLM APIs: Logs, quality metrics, latency

3

Bias, hallucination, and harmful response filters

4

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

5

Cost control patterns: Dynamic model fallback, temperature tuning

1

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

2

Code Annotator: JS plugin for live code explanations

3

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

4

Dev CLI Agent: Chat-style AI CLI command generator

5

Copy Generator: AI for personalized product copywriting

6

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

Meet your Trainer

Our Trainers

Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.

We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.

Our Trainers

Learning Outcomes

Upon finishing the training, you will:

1

Gain expertise in using LangChain and LlamaIndex to create powerful AI-powered tools and applications

2

Apply vector embeddings and build RAG (Retrieval-Augmented Generation) systems to improve AI model effectiveness

3

Fine-tune and customize AI models for specific use cases using PEFT techniques and the Hugging Face ecosystem

4

Build scalable applications with practical experience in the OpenAI API, including integration and deployment

5

Design intelligent solutions through prompt engineering, model tuning, and efficient API management

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KHDA Certificate

Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.

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Learners Point Certificate

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

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Frequently asked questions

Our Applied AI for Software Developers Certification in Qatar helps developers apply AI in real software projects. It focuses on tools like LangChain, GPT, and model fine-tuning, teaching how to incorporate AI into existing workflows. This course boosts your resume and increases your chances of securing roles in tech industries across the GCC and other competitive regions.

Yes, our Applied AI for Software Developers course in Doha teaches you how to use tools like OpenAI API, LangChain, and LlamaIndex. These help you build AI functionalities that can be integrated into mobile apps. Whether you're developing AI-based chat assistants or smart summarisation tools, this training gives you practical skills to apply AI without needing a deep ML background.

This program helps you build AI-based tools for log summarisation, test case generation, and automated issue detection using prompt engineering and the OpenAI API. These skills are highly valuable for modern QA and test automation roles in Qatar. Here, organizations are increasingly adopting AI-enhanced workflows to improve software quality and efficiency.

This training is ideal for software developers who want to grow their careers in AI. If you're looking to learn tools like LangChain, LlamaIndex, and Hugging Face and apply them to build smart applications, this course is for you. It’s perfect for those aiming to work on real-world projects and lead AI development efforts.

This program teaches the practical use of advanced tools such as LangChain, LlamaIndex, OpenAI API, PEFT, and Hugging Face. Developers also learn to work with vector embeddings, build RAG systems, and create AI-powered apps. Hands-on projects make sure you gain real-world experience while mastering how to fine-tune AI models for different industry use cases.

Our graduates are prepared for roles such as:

1. AI Developer
2. Machine Learning Engineer
3. Data Scientist
4. AI Solutions Architect

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