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Prompt Templating and Techniques in LangChain

Name: Prompt Templating and Techniques in LangChain
Uploaded: 2025-06-11T12:30:53.000Z
Duration: 29 min 56 s
Channel: James Briggs
Description: Tutorial on prompt templating and advanced prompting techniques in LangChain, including dynamic prompts, few-shot prompting, and chain-of-thought reasoning for LLM applications.

James Briggs16 June 2026Watch on YouTube

Part of series

Ep. 7 · Langchain Full Agent

View the series

What you'll learn

Instruction fine-tuned LLMs made it possible to adapt models through prompts instead of modifying their weights.
Prompt templating in LangChain allows you to build dynamic prompts that adapt based on various parameters.
Few-shot prompting and chain-of-thought reasoning are advanced techniques to improve LLM application performance.
LangChain provides built-in functionalities to structure and automatically adjust prompts in RAG pipelines and other AI workflows.

Frequently asked questions

What is the difference between prompt engineering and fine-tuning models?

Before 2021, you had to adjust model weights with large datasets. Instruction-fine-tuned LLMs changed this by adapting models through prompts instead of training.

How does LangChain help with building dynamic prompts?

LangChain provides functionalities to structure prompts so that content and format automatically adjust based on parameters you set.

What is few-shot prompting and when do you use it?

Few-shot prompting is an advanced technique where you include examples in the prompt to better guide the LLM toward your desired output.

How does chain-of-thought reasoning help in LLM applications?

Chain-of-thought reasoning is a technique that prompts the LLM to reason step-by-step, often resulting in better and more accurate answers.

Topics

langchain prompting llm rag tutorial

Description from the channel

Until 2021, to use an AI model for a specific use case, we would need to fine-tune the model weights themselves. That would require huge training data and significant computate to fine-tune any reasonably performing model. Instruction-fine-tuned large language models (LLMs) changed this fundamental rule of applying AI models to new use cases. Rather than needing to either train a model from scratch or fine-tune an existing model, these new LLMs could adapt incredibly well to a new problem or use case with nothing more than a prompt change. Prompts allow us to completely change the functionality of an AI pipeline. Through natural language, we tell our LLM what it needs to do, and with the right AI pipeline and prompting, it often works. LangChain naturally has many functionalities geared towards helping us build our prompts. We can build dynamic prompting pipelines that modify the structure and content of what we feed into our LLM based on essentially any parameter we would like. In this example, we'll explore the essentials of prompting in LangChain and apply this in a demo Retrieval Augmented Generation (RAG) pipeline. 🔗 Full Course: https://www.aurelio.ai/course/langchain 📌 Article and code: https://www.jamesbriggs.io/langchain-prompts 👋🏼 AI Consulting: https://aurelio.ai 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ #ai #coding #aiagents #langchain 00:00 Prompts are Fundamental to LLMs 02:13 Building Good LLM Prompts 07:13 LangChain Prompts Code Setup 11:36 Using our LLM with Templates 16:54 Few-shot Prompting 23:11 Chain of Thought Prompting