Build AI Apps with DeepSeek APIs Training in Dhaka, Bangladesh

The Build AI Apps with DeepSeek APIs Training is a hands-on, developer-focused program designed to teach how to build production-grade AI applications using DeepSeek APIs.
The course focuses on API integration, AI app architecture, prompt design, structured outputs, workflow orchestration, tool integration, and deployment, enabling teams to create scalable, secure, and enterprise-ready AI systems.

This training is ideal for software developers, AI engineers, backend engineers, full-stack developers, and technical architects building real-world AI-powered applications.

Introduction to AI Application Development

• AI application architecture patterns

• LLM-based system design concepts

• Where DeepSeek fits in AI stacks

DeepSeek API Fundamentals

• API authentication and setup

• Request and response structures

• Handling errors and retries

• Managing tokens and limits

Project Setup & Environment Configuration

• Creating an AI app project

• Environment variables and secrets

• Config management

• Secure API usage practices

Prompt Engineering for API-Based AI Apps

• Designing API prompts

• Context injection strategies

• Role-based prompting

• Prompt templates for reuse

Structured Outputs & Validation

• JSON and schema outputs

• Enforcing formats

• Output parsing and validation

• Handling malformed responses

Building AI Services & Microservices

• AI backend service design

• API-first AI architecture

• Stateless vs stateful AI services

• AI service scaling concepts

Multi-Step AI Workflows

• Chained AI calls

• Conditional logic and branching

• Workflow orchestration patterns

• State management in AI apps

AI Agents with DeepSeek APIs

• Agent concepts and design

• Task-based and goal-based agents

• Memory and context handling

• Human-in-the-loop patterns

RAG (Retrieval-Augmented Generation)

• Vector databases (conceptual)

• Knowledge base integration

• Document retrieval pipelines

• Grounded AI responses

Performance & Cost Optimization

• Token optimization strategies

• Caching and reuse

• Latency reduction techniques

• Cost monitoring and control

Security, Privacy & Governance

• API security best practices

• Secrets management

• Data protection strategies

• AI governance models

Testing & Quality Assurance

• Testing AI outputs

• Prompt regression testing

• Mocking AI responses

• Reliability engineering for AI apps

Deployment & Production Operations

• Deploying AI applications

• CI/CD for AI services

• Monitoring and observability

• Logging and tracing AI workflows

Hands-on Exercises

Summary and Conclusion