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In this lesson, we’ll explore the essential components for creating robust, enterprise-grade applications with LangChain, OpenAI, or any other LLM framework. Whether you’re leveraging Microsoft Copilot, Google Gemini, or OpenAI’s ChatGPT, you’ve interacted with a polished interface that seamlessly manages prompts, context, and conversation history. To achieve similar reliability and scalability, let’s break down what happens behind the scenes.
The image is a flowchart illustrating the key building blocks of an LLM (Large Language Model) application, showing the interaction between the user, application, context, language model, and history.

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Core Components

ComponentRoleExample
User InterfaceCaptures input from end-users via web, mobile, or chat UIChat widget, web form
Prompt GenerationTransforms user input into a structured prompt suitable for the LLMTemplate-based or dynamic prompt builder
Context ManagementSupplies additional data—documents, user profile, or external APIsPDF upload, database lookup
Language ModelExecutes the prompt on an LLM (e.g., GPT-3.5, GPT-4) to generate a responseopenai.ChatCompletion.create
Response HandlingProcesses, formats, and presents the LLM output to the userJSON parsing, HTML rendering
History ManagementStores and retrieves past conversations to maintain context and continuityDatabase, in-memory session cache
Providing rich, relevant context is key to minimizing hallucinations and improving answer accuracy.

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ChatGPT Interface Example

The following diagram shows how ChatGPT ties all the building blocks together in a real-world user interface:
The image is a labeled diagram of the ChatGPT interface, highlighting components such as the language model, prompt, response, history, and context.
UI ElementDescription
Language Model SelectorSwitch between GPT-3.5 or GPT-4
Prompt InputEnter queries like “Suggested ideal diet plan for a rookie runner.”
Context UploadAttach files—PDFs, CSVs—to refine model output
Response PanelDisplays the generated answer
History SidebarShows previous conversations for continuity
Always handle user data securely and comply with GDPR, CCPA, or other regional regulations when storing conversation history.

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Sample Code Snippet

Below is a simple example using LangChain and OpenAI’s Python API to create a chat completion: 
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

# Initialize the chat model
chat = ChatOpenAI(model_name="gpt-4", temperature=0.7)

# Create a human message prompt
messages = [
    HumanMessage(content="Suggest an ideal diet plan for a rookie runner.")
]

# Send the prompt and receive the response
response = chat(messages)
print(response.content)

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What’s Next

In the upcoming sections, we’ll dive deeper into each component:
  • Prompt engineering best practices
  • Incorporating external knowledge sources
  • Advanced context management patterns
  • State persistence and session orchestration

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