PluralSight – Memory, State, and Conversational Applications in LangChain 2026
English | Tutorial | Size: 264.88 MB
Large language models quickly lose coherence in real conversations without memory. This course will teach you how to design, implement, and manage memory in LLM applications to build reliable, multi-turn conversational systems.
What you’ll learn
Stateless large language models struggle to maintain context, consistency, and relevance across long conversations, leading to forgotten instructions, broken references, and rising token costs. In this course, Memory, State, and Conversational Applications with LangChain, you’ll gain the ability to design robust conversational AI systems that reason coherently over time. First, you’ll explore why LLMs are stateless by design and how explicit memory layers restore continuity through short-term and long-term memory strategies. Next, you’ll discover how LangChain’s memory abstractions work in practice, including buffer, windowed, summarized, entity-based, and vector-backed memory implementations, along with their tradeoffs. Finally, you’ll learn how to build production-ready multi-turn conversational applications that manage context windows, control costs, persist user state, and mitigate risks like context drift, privacy leakage, and inconsistent responses. When you’re finished with this course, you’ll have the skills and knowledge of LLM memory management and conversational state design needed to design, evaluate, and operate memory-aware conversational AI systems that scale reliably in real-world applications.
DOWNLOAD: