Adaptive Email Agents with DSPy

  • BeginnerLevel

  • 121+Students Enrolled

  • 45 MinsDuration

  • 4.7Average Rating

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About this Course

  • Learn how to build adaptive email agents using DSPy that evolve beyond static prompts, integrating retrieval, optimization, and context awareness for smarter, human-like responses.
  • Understand context engineering, the foundation of adaptive systems & how DSPy helps design intelligent workflows that select, compress, and apply relevant information effectively
  • Explore DSPy’s modular workflow with datasets, signatures, and evaluation metrics to create robust agents capable of reasoning, retrieval, and dynamic interaction with real data.
  • Gain hands-on experience in optimizing prompts using DSPy’s MePro v2 and random sampling techniques to improve agent accuracy, reduce hallucinations, and enhance adaptability.

Learning Outcomes

Build Adaptive Agents

Create intelligent email agents with DSPy that learn and respond

Learn Context Engineering

Apply R-S-C-I principles to manage memory, retrieval, and compression

Optimize with DSPy Tools

Use MePro v2 and sampling to refine prompts and boost agent accuracy

Who Should Enroll

  • Data scientists and AI developers eager to build adaptive agents using DSPy and LLM workflows.
  • Professionals exploring context engineering and smart prompt optimization for real-world AI use.
  • Engineers and researchers aiming to integrate retrieval, reasoning, and response automation in agents.
  • Learners who want hands-on experience in building intelligent, self-learning email automation systems.

Course Curriculum

Explore DSPy fundamentals, context engineering, and adaptive workflows. Build smart email agents, apply retrieval and optimization, and gain hands-on experience creating self-learning LLM systems that evolve with real-world data.

tools

  1. 1. Course Overview & What you will Build with DSPy

  2. 2. Context Engineering in DSPy: Making Agents Smarter

  3. 3. Designing Smart Email Agents: DSPy Workflows

  1. 1. Building your first Adaptive Email Agent with DSPy

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Praneeth Paikray

Sr. Generative AI Specialist

Praneeth architects and deploys innovative Data Science and GenAI solutions across diverse domains, building recommender systems, fine-tuning LLMs, and creating Agentic AI applications. He is driven by collaboration and real-world impact.

Get this Course Now

With this course you’ll get

  • 45 Mins

    Duration

  • Praneeth Paikray

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

DSPy is a declarative framework for optimizing LLM prompts and workflows. It helps you design adaptive agents that automatically refine their performance through data-driven optimization and evaluation.

Basic Python knowledge helps, Though, this course provides guided explanations, real-world examples, and hands-on practice to make DSPy and LLM concepts easy to understand.

You’ll build an adaptive email agent that can interpret user queries, fetch relevant data, and compose intelligent replies using retrieval, reasoning, and optimization modules in DSPy.

Yes. DSPy supports OpenAI, Hugging Face, Azure AI Foundry, Amazon Bedrock, and other models, allowing you to build agents on your preferred platform.

While LangChain and CrewAI focus on chaining LLM tools, DSPy emphasizes optimization and declarative design, automatically improving prompt logic and agent performance through measurable metrics.

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