> ## Documentation Index
> Fetch the complete documentation index at: https://langchat.neurobrains.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Custom Prompts

> Control how your chatbot thinks and speaks with custom prompt templates.

LangChat uses two prompt templates internally:

1. **System prompt** — defines the chatbot's persona, task, and how to use context
2. **Standalone question prompt** — reformulates follow-up questions into self-contained queries before searching

Both have sensible defaults. Override either or both.

***

## System prompt

The system prompt is built once per message. It contains three required placeholders:

| Placeholder      | Filled with                                     |
| ---------------- | ----------------------------------------------- |
| `{context}`      | Top retrieved document chunks (after reranking) |
| `{chat_history}` | Recent conversation exchanges                   |
| `{question}`     | The user's current message                      |

### Default template

```
You are a helpful AI assistant. Answer questions clearly and accurately.

Use the following context to answer:
{context}

Previous conversation:
{chat_history}

User question: {question}

Your response:
```

### Custom template

Pass `prompt_template` when creating `LangChat`:

```python theme={null}
from langchat import LangChat
from langchat.providers import OpenAI, Pinecone, Supabase

MY_PROMPT = """You are Aria, a friendly support agent for Acme Corp.
Always be concise and professional. If you don't know the answer, say so clearly
and suggest contacting support@acme.com.

Relevant knowledge base articles:
{context}

Previous messages:
{chat_history}

Customer: {question}

Aria:"""

lc = LangChat(
    llm=OpenAI("gpt-4o-mini"),
    vector_db=Pinecone("my-index"),
    db=Supabase(),
    prompt_template=MY_PROMPT,
)
```

<Warning>
  Your template must contain all three placeholders — `{context}`, `{chat_history}`, and `{question}` — or the chain will raise an error.
</Warning>

### Domain-specific examples

<CodeGroup>
  ```python Legal assistant theme={null}
  LEGAL_PROMPT = """You are a legal research assistant. Provide accurate information
  based on the provided documents. Always note that this is not legal advice and
  users should consult a qualified attorney for legal decisions.

  Relevant legal documents:
  {context}

  Prior conversation:
  {chat_history}

  Question: {question}

  Legal Assistant:"""
  ```

  ```python Technical documentation bot theme={null}
  TECH_PROMPT = """You are a technical documentation assistant for {product_name}.
  Answer questions using only the provided documentation. If the documentation
  doesn't cover the question, say so clearly.

  Documentation context:
  {context}

  Conversation history:
  {chat_history}

  Developer question: {question}

  Documentation Assistant:"""
  ```

  ```python E-commerce product assistant theme={null}
  ECOMMERCE_PROMPT = """You are a shopping assistant. Help customers find products
  and answer questions about inventory, shipping, and returns based on the provided
  product catalog information.

  Product catalog context:
  {context}

  Customer conversation:
  {chat_history}

  Customer: {question}

  Shopping Assistant:"""
  ```
</CodeGroup>

***

## Standalone question prompt

Before searching Pinecone, LangChat reformulates the user's message to be self-contained. This handles follow-up questions like:

* "What about the pricing?" → "What is Acme Corp's pricing?"
* "Can you explain that further?" → "Can you explain Acme Corp's refund policy further?"

### Default behavior

The default prompt:

1. Uses the LLM to rephrase the question
2. Keeps greetings unchanged (hi, hello, hey)
3. Always generates questions in English

### Custom standalone question prompt

```python theme={null}
STANDALONE_PROMPT = """Given the following conversation and a follow-up question,
rephrase the follow-up as a standalone question that includes all necessary context.
If the follow-up is a greeting, return it unchanged.

Conversation:
{chat_history}

Follow-up: {question}

Standalone question:"""

lc = LangChat(
    llm=OpenAI("gpt-4o-mini"),
    vector_db=Pinecone("my-index"),
    db=Supabase(),
    standalone_question_prompt=STANDALONE_PROMPT,
)
```

The standalone question prompt uses two placeholders: `{chat_history}` and `{question}`.

***

## Prompt tips

**Be specific about tone.** "Be concise and professional" produces very different results than the default.

**Tell the model what to do when it doesn't know.** If you don't specify, it may hallucinate. Add: *"If the answer is not in the context, say you don't know."*

**Set the output format.** If you need structured output: *"Always respond with bullet points."* or *"Answer in 2-3 sentences maximum."*

**Keep `{context}` early.** Models attend more strongly to content near the start of the prompt.

**Test with `verbose=True`.** See exactly what prompt is being sent:

```python theme={null}
lc = LangChat(
    ...,
    verbose=True,  # logs the full prompt on every call
)
```