Concepts, Parameters, Streaming, Caching, and a Real Enterprise Example
Prompting in Anthropic (Claude) is not just about writing good instructions.
In production systems, prompts behave more like specifications than conversations.
This article explains how prompting actually works in Anthropic, how to design reliable and compliant prompts, and how to use JavaScript to build real systems—ending with a production-grade sentiment analysis prompt.
1. How Anthropic Prompting Really Works
Anthropic uses a message-based API.
Each request contains the entire conversation state.
Claude does not remember anything between API calls.
Mental model
You → send full conversation
Claude → continues from that context
Every request must include:
Who the assistant is
What rules apply
What the user just said
Any images or documents
2. Messages and Roles (Core Concept)
Anthropic uses three roles:
| Role | Purpose |
system | Hard rules, behavior, constraints |
user | Input data or tasks |
assistant | Previous model replies (conversation priming) |
Example
messages: [
{ role: "user", content: "Hello! Only speak Spanish." },
{ role: "assistant", content: "Hola!" },
{ role: "user", content: "How are you?" }
]
Claude will answer in Spanish because:
Production rule
Put non-negotiable rules in system, not in conversation history.
3. System vs User Instructions (Why It Matters)
| Instruction Type | Reliability |
| System | Strong, persistent |
| User | Soft, overrideable |
| Assistant | Priming only |
Production-safe example
system: "You must respond only in Spanish."
This survives:
4. Temperature: Controlling Randomness
Temperature controls how predictable Claude’s word choices are.
It does not control intelligence.
| Temperature | Behavior | Use case |
| 0.0–0.2 | Deterministic | JSON, analytics |
| 0.3–0.5 | Controlled | Summaries |
| 0.6–0.8 | Creative | Writing |
| 0.9+ | Unstable | Rarely useful |
Example
temperature: 0.0
Use this when:
5. stop_sequences: When the Model Must Stop
stop_sequences tells Claude when to stop generating.
stop_sequences: ["\nUser:"]
Claude stops immediately when it reaches that string.
Common uses
Prevent role leakage
End JSON cleanly
Stop agent loops
Key rule
Stop sequences are guards, not fixes for bad prompts.
6. Streaming Responses (Real-Time Output)
Streaming lets Claude send text incrementally.
with client.messages.stream(...) as stream:
for text in stream.text_stream:
print(text)
Why streaming exists
When NOT to stream
JSON APIs
Tool calls
Strict schema validation
Production pattern
let buffer = "";
for (const chunk of stream.text_stream) {
buffer += chunk;
process.stdout.write(chunk);
}
7. Multimodal Prompting (Images + Text)
Anthropic allows image + text together.
{
role: "user",
content: [
{ type: "image", source: {...} },
{ type: "text", text: "Count the containers." }
]
}
Rules
8. Prompt Caching and cache_control
Anthropic optimizes performance by caching repeated prompt prefixes.
This is good for:
System prompts
Templates
Policies
But dangerous for:
User data
Transcripts
Images
PII
9. cache_control: { type: "ephemeral" }
"cache_control": { "type": "ephemeral" }
This means:
Use this content once. Do not cache. Do not reuse. Do not persist.
When to use it
| Data | Ephemeral |
| User input | ✅ |
| Call transcripts | ✅ |
| Images | ✅ |
| Customer feedback | ✅ |
Why it matters
10. JavaScript Example: Safe Prompt with Ephemeral Data
import Anthropic from "@anthropic-ai/sdk";
const client = new Anthropic({
apiKey: process.env.ANTHROPIC_API_KEY
});
const response = await client.messages.create({
model: "claude-3-5-sonnet-20240620",
max_tokens: 200,
temperature: 0.0,
system: "You are a deterministic analysis engine.",
messages: [
{
role: "user",
content: [
{
type: "text",
text: "The service was slow and frustrating.",
cache_control: { type: "ephemeral" }
}
]
}
]
});
console.log(response.content[0].text);
11. What Makes a Prompt Production-Grade
A production prompt has:
Clear role definition
Explicit constraints
Defined output format
Fail-closed behavior
Low temperature
No exposed reasoning
Safe handling of sensitive data
Good prompts are specifications, not conversations.
12. Production-Grade Sentiment Analysis Prompt
This prompt follows all best practices discussed above.
Prompt
Analyze the sentiment of the following customer message.
Rules:
- Classify sentiment as POSITIVE, NEGATIVE, or NEUTRAL
- Do not include personal data
- Do not infer intent beyond the text
- Limit text fields to 100 characters
Output:
Return ONLY valid JSON.
No explanations.
Schema:
{
"sentiment": "POSITIVE | NEGATIVE | NEUTRAL",
"confidence": 0.0-1.0,
"summary": "string"
}
If sentiment cannot be determined, return:
{
"sentiment": "NEUTRAL",
"confidence": 0.0,
"summary": "Insufficient data"
}
13. Sentiment Analysis: Full JavaScript Example
const response = await client.messages.create({
model: "claude-3-5-sonnet-20240620",
max_tokens: 150,
temperature: 0.0,
system: "You are a strict sentiment classification engine.",
messages: [
{
role: "user",
content: [
{
type: "text",
text: "Support was slow and unhelpful.",
cache_control: { type: "ephemeral" }
}
]
}
]
});
console.log(response.content[0].text);
14. Final Takeaway
Anthropic prompting works best when you treat prompts like contracts: explicit, constrained, deterministic, and safe.
