Agentic AI: Deliver a successful chatbot PoC with LangGraph

Define expected interactions and indicative dialogues for the chatbot, specify functional and technical specifications for the proof of concept, and map a conceptual architecture showing how building blocks interact.

Specify indicative dialogues to define the chatbot's expected interactions, guiding the Agentic graph structure, tools, and prompts for a customer support demo in the energy sector.

Define functional specs for a chatbot that understands needs, and recommends electricity plans by consumption and budget, retrieves past bills with filters, provides natural dialogue, and authenticates data for PoC.

Define technical specifications for a proof-of-concept chatbot, including natural language understanding, dynamic routing of billing and electricity plan queries to text processing modules, with database integration and response latency considerations.

Explore a conceptual architecture where user prompts feed a Landgraf graph that orchestrates LLM calls and tools, enabling database access, adaptive prompting, routing to subgraphs, and multi-provider compatibility.

Launch and optimize a customer support bot for a LangGraph-powered PoC, applying agentic AI principles to design, test, and evaluate practical chatbot interactions.

Iterate lightweight adjustments from user feedback to improve reliability, addressing misclassification and out-of-scope inputs, by applying fine tuning across prompts, tools, and Landgraf graph structure.

Explore core prompt engineering strategies, including zero-shot, few-shot, and chain of thought prompting, to improve a LangGraph chatbot's accuracy, coherence, and task routing, with testing to prevent misclassification.

Learn to fine-tune tools by simplifying arguments, using distinct tool scopes, and programmatically constructing queries with date time arguments to improve tool selection for Agentic AI chatbot PoC.

Explore how to fine-tune the LangGraph graph structure to prevent wrong tool calling by implementing deterministic subgraphs and balancing consistency with natural conversation.

Section intro outlines taking a large language model chatbot from proof of concept to production with guardrails, caching, profiling, and in-house infrastructure, plus context compression for long contexts.

Define and implement guardrails across input, tool calling, and output to ensure safe, ethical, and scoped chatbot responses, using intent classification, filtering, and moderation.

Explore performance optimization for production-ready chatbots by applying large language model heterogeneity, infrastructure tuning, context window and output tokens tradeoffs, graph design, and caching.

Profile resource configurations across load levels to understand latency and time-to-first-token, and develop a sizing mechanism that translates performance constraints into scalable cloud hardware plans and scaling strategies.

Explore large language model infrastructure with Olama on a four-graphics-card system, monitor processor allocation with Olama PS, and balance load across GPUs to support production readiness.

Learn techniques to manage long context in a LangGraph chatbot PoC by summarizing user inputs, trimming chat history, and optimizing system prompts to balance memory use and response latency.

Anticipate client concerns during the chatbot demo by outlining tested interactions, addressing incorrect responses, and explaining how larger language models, retrieval augmented generation, and deterministic workflows improve accuracy and consistency.

Outline the PoC workflow from preparation to production readiness. Leverage representative interactions to define functional and technical specs, design high level architecture, and apply versatile agentic patterns.