> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Demo Using Dynamic Context with Prompt
> This tutorial covers dynamic context injection for chatbots to provide accurate, up-to-date answers by sourcing relevant information at runtime.
In this tutorial, we’ll cover **dynamic context injection**—a technique that ensures your chatbot always provides accurate, up-to-date answers by pulling relevant information at runtime. You’ll start with a basic chat application using the [OpenAI Chat Completion endpoint][openai-chat] and evolve it into a robust system that sources context dynamically.
## 1. Basic Chat Completion (No External Context)
First, observe how GPT-3.5-turbo handles a query about the 95th Academy Awards (March 2023) without any added context. Because its training data cuts off in September 2021, it won’t know about later events.
```python theme={null}
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{
"role": "system",
"content": "You answer questions about the 95th Academy Awards held in March 2023. Answer only if you are sure."
},
{
"role": "user",
"content": "Which movie won the Best Picture award?"
}
]
)
print(response.choices[0].message.content)
```
Expected response:
```text theme={null}
I’m sorry, but I don’t have information on events after September 2021.
```
Without external context, the model defaults to stating its knowledge cutoff.
## 2. Injecting Static Context
To work around the cutoff, you can paste relevant excerpts from a trusted source directly into the prompt. For instance, from [Good Morning America][gma]:
> On Hollywood’s biggest night, *Everything Everywhere All at Once* reigned supreme, winning seven Oscars, including Best Picture.
Inject this snippet into your user message:
```python theme={null}
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{
"role": "system",
"content": "You answer questions about the 95th Academy Awards held in March 2023. Answer only if you are sure."
},
{
"role": "user",
"content": (
"On Hollywood’s biggest night, \"Everything Everywhere All at Once\" reigned supreme, "
"winning seven Oscars, including Best Picture. "
"Which movie won the Best Picture award?"
)
}
]
)
print(response.choices[0].message.content)
```
Output:
```text theme={null}
"Everything Everywhere All at Once" won the Best Picture award at the 95th Academy Awards.
```
Static context can quickly bloat your prompt and is tedious to maintain as information changes.
## 3. Why You Need Dynamic Context
Manual context updates are not scalable. Instead, build a pipeline that:
| Step | Description |
| ---------------------------- | ---------------------------------------------------------------- |
| 1. Store documents | Save articles, transcripts, or FAQs in a vector database |
| 2. Embed user queries | Generate embeddings for incoming questions |
| 3. Retrieve relevant context | Perform a similarity search to fetch the most pertinent passages |
| 4. Inject into the prompt | Append retrieved snippets dynamically before calling the API |
This **dynamic context** approach keeps your chatbot current without manual prompt edits.
## 4. Next Steps
In the following sections, we’ll demonstrate how to:
* Generate embeddings for your documents
* Set up and query a vector store (e.g., Pinecone, FAISS)
* Compose the final prompt with retrieved context and call the Chat Completion endpoint
Stay tuned for the detailed implementation guide!
## Links and References
* [OpenAI Chat Completion Guide][openai-chat]
* [Good Morning America Oscars Coverage][gma]
[openai-chat]: https://platform.openai.com/docs/guides/chat
[gma]: https://abcnews.go.com/GoodMorningAmerica/