This course is part of the Generative AI and LLMs ExpertTrack
Building LLM Applications with APIs and Frameworks
Gain practical LLM application development skills and understand how to create reliable generative AI applications with Python.
Duration
3 weeksWeekly study
3 hours100% online
How it worksIncluded in an ExpertTrack
Course 2 of 4
Building LLM Applications with APIs and Frameworks
Build LLM applications with leading APIs and frameworks
Generative AI becomes truly useful when you can turn prompts and models into working applications. On this three-week course, you’ll learn how to build LLM applications using leading APIs and frameworks, developing practical skills for modern generative AI development.
You’ll work with LLM APIs from OpenAI, Anthropic, Gemini, and Mistral, as well as open-source models. By the end of the course, you’ll have the expertise to build LLM-powered applications using commercial APIs and leading frameworks, preparing you for roles in LLM application development and generative AI.
Develop applications with LangChain and LlamaIndex
Explore the frameworks that support modern LLM application development. You’ll use LangChain to build chains, tools, and memory, while using LangChain Expression Language to structure application logic.
You’ll also work with LlamaIndex to ingest documents, build indexes, and query information. By combining these frameworks, you’ll learn how to connect LLMs with data and tools to create more capable AI applications.
Build reliable and production-ready AI applications
Develop the skills needed to move beyond experimentation and build more dependable LLM applications. You’ll learn how to select models by balancing cost, latency, and capability, while applying caching, retries, logging, and observability to improve reliability.
Through hands-on API integration, parameter tuning, and multi-step chains, you’ll put your learning into practice. The course culminates in a project where you’ll build a working LLM application with tool integration.
Syllabus
Week 1
LLM API Integration and Generation Controls
Calling LLM APIs Across Providers
Explore how Edureka’s course connects applications with LLM APIs across providers, comparing models based on performance, cost, speed, features, security, and flexibility.
Configuring Generation and Managing Tokens
Learn with Edureka how to configure LLM generation settings, understand tokens, manage response length, and optimise AI outputs for different tasks, costs, and performance.
Structured Outputs and Tool Calling
This Edureka course explores structured outputs and tool calling, showing how LLMs can generate reliable responses and interact with external tools to build practical Generative AI applications.
Week 2
Building Applications with LangChain and LlamaIndex
Composing Applications with LangChain
In this Edureka course, learn how LangChain helps combine LLMs, prompts, tools, and workflows to build practical Generative AI applications.
Document Ingestion and Querying with LlamaIndex
In this Edureka course, learn how LlamaIndex enables document ingestion and querying to build efficient and context-aware Generative AI applications.
Week 3
Model Selection, Reliability, and the Capstone
Selecting the Right Model
In this Edureka course, learn how to select the right AI model based on application requirements, capabilities, and performance needs.
Reliability, Observability, and the Capstone
In this Edureka course, learn how to improve GenAI application reliability, monitor system behaviour, and apply these practices in a capstone project.
Prove you're job ready
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- Learn the latest in your chosen industry or subject.
- Complete each course and pass assessments.
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- Make your career dreams a reality.
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Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- Explain how LLM APIs, generation parameters, structured outputs, and tool calling support application development.
- Apply API integration techniques to configure model behaviour, stream responses, and manage token usage and cost.
- Develop LLM workflows using LangChain and LlamaIndex with chains, tools, memory, document ingestion, and querying.
- Evaluate models and application approaches based on cost, latency, capability, reliability, and output quality.
- Create an end-to-end LLM application that integrates models, tools, reliability mechanisms, and observability practices.
Who is the course for?
This course is designed for learners who understand how LLMs work and can design effective prompts and who now want to develop working GenAI applications in Python.
It is ideal for developers building AI features, data professionals creating LLM-powered tools, and engineers preparing for LLM application developer roles. Learners should be comfortable with Python and prompt engineering before starting. By the end, they will be able to build LLM-powered applications using commercial APIs and orchestration frameworks.
Who will you learn with?
Who developed the course?
Edureka
Edureka is a global online learning platform offering industry-focused training in emerging technologies such as Cloud, AI, GenAI, Data Science, DevOps, and Cybersecurity, helping professionals upskill and advance their careers.
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