Prompt Engineering & AI Agent Development Training in Vizag
Build clearer prompts and more dependable AI workflows with Prompt Engineering and AI Agent Development training in Vizag at Softenant Technologies. Study instructions, context, structured outputs and evaluation, then connect those skills to tool calling, workflow state, memory boundaries and human approval. This course helps learners in Visakhapatnam connect prompt design with practical agent-development decisions.
From prompt design to AI agent workflows
Learners test prompts against difficult inputs, request structured results, define tool permissions and build workflows that know when to ask for clarification or hand work to a person.
Prompt engineering and AI agent syllabus
- System prompts, examples and output schemas. Define the task, relevant context, constraints and output format. Compare prompts with and without examples, then check whether the result follows the requested structure.
- Prompt evaluation and failure cases. Create test inputs with expected outcomes. Include ambiguous requests, missing information and unsupported claims; record which prompt changes improve results and which introduce new failures.
- Tool-calling and agent workflow concepts. Describe each tool’s purpose, input requirements and possible errors. Separate predictable task-routing workflows from tasks that require the model to choose its next step.
- State, memory and approval boundaries. Decide what information a workflow should retain and when it should ask for clarification. Require human review before consequential actions and define a clear stopping condition.
- Automation design and safety checks. Validate tool inputs and structured results, handle failed steps and document recovery paths. Review the whole workflow instead of judging only one successful response.
Project evidence
How practical work is assessed
Projects are reviewed against defined inputs, implementation choices, test cases, output quality and documented limitations. Learners should be able to explain the workflow, not just show a final screen or code sample.
Trusted by Softenant Technologies Students
Read Our Google ReviewsPreparation and learning path
Start with one well-defined task and a small set of test cases. Basic Python, JSON and API knowledge are useful preparation for the implementation work; prompt-writing practice itself can begin with plain-language instructions. Ask about the starting level that fits your background.
For model foundations, explore Generative AI and LLM training. For answers grounded in retrieved documents, see RAG systems training. These topics support, but do not replace, careful prompt evaluation and tool permissions.
AI agent implementation guide
For an implementation example, read our Python AI agent guide to tool calling and validation. Use its workflow checks alongside a small, repeatable prompt-evaluation set.
Prompt engineering course FAQs
What does the prompt engineering course cover?
The syllabus covers instructions, context, examples, structured outputs, prompt evaluation and failure cases. AI agent development adds tool calling, workflow state, memory boundaries and human approval steps.
How is an AI agent different from a prompt?
A prompt supplies instructions and context to a model. An agent workflow can also select tools, track state and act on intermediate results. Reliable designs define permissions, stopping conditions and when a person should review the next action.
Do I need programming knowledge before learning prompt engineering?
You can practise writing and testing prompts without building an application. Basic Python, JSON and API concepts are useful preparation for implementing tool calls and agent workflows. Discuss your starting level before choosing a project.
Which practical projects are covered?
Project areas include an email-classification prompt suite, a research assistant with citation rules, a human-approved task-routing agent and a tool-use workflow with error handling. Evaluation should record test inputs, expected behaviour, failures and limitations.
How does this course relate to generative AI and RAG?
This course focuses on instructions, evaluation and tool-driven workflows. Generative AI and LLM foundations cover the broader model context; retrieval-augmented generation focuses on retrieving source material to support answers. The related courses below cover those separate learning paths.
Discuss your learning goals
Contact Softenant Technologies to discuss the syllabus, your current skills and suitable project goals. Call +91 9393969628 or send a WhatsApp enquiry.
Visit: Flat No. 101, Geetha Mansion II, Junction, opposite Andhra Bank, Akkayyapalem, Visakhapatnam, Andhra Pradesh 530016, India.