Your Llama 3.3 70B prompt works on Together AI. You move inference to Ollama on a GPU box in the office. Same model family, same chat format, but someone pasted an older system prompt into the Ollama service config six weeks ago. Staging and production now disagree, and neither matches the version in the repo you stopped updating when you switched hosts.

Open-source models multiply deployment targets. Llama prompt management keeps one versioned source of truth for system instructions regardless of whether inference runs on Ollama, llama.cpp, Groq, Together AI, AWS Bedrock, or vLLM.

What makes Llama different from hosted APIs

Hosted providers (OpenAI, Anthropic, Mistral) have one official endpoint. Llama has an ecosystem:

Ollama on a developer laptop or internal server
llama.cpp server for edge and embedded
Groq, Together AI, Fireworks for hosted open models
vLLM / TGI in your Kubernetes cluster
AWS Bedrock for managed Llama access

The inference URL changes. The system prompt should not live in each place separately.

Prompt management for Llama means: store the instruction text centrally, fetch via HTTPS, pass to whichever endpoint you call. PromptForge returns plain text. Your routing to Ollama vs Groq is application config.

See LLM-Specific Prompt Management for the full provider map.

Message format: OpenAI-compatible vs raw tokens

Most Llama deployments today use the OpenAI-compatible chat format:

{
  "model": "llama3.3",
  "messages": [
    { "role": "system", "content": "You are a helpful assistant." },
    { "role": "user", "content": "Hello" }
  ]
}

Ollama (/v1/chat/completions), Groq, Together, and most hosted providers accept this. Store plain system-prompt text in PromptForge.

If you call llama.cpp's raw completion endpoint with special tokens (<|begin_of_text|>, header IDs, etc.), store the full formatted string including delimiters. PromptForge returns it verbatim. Know which format your runtime expects.

Integration example: Ollama locally

async function fetchLlamaPrompt(context: string, language: string) {
  const res = await fetch(
    "https://www.promptforge-app.com/api/v1/prompts/your-prompt-id",
    {
      method: "POST",
      headers: {
        Authorization: "Bearer pfk_your_api_key",
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        version: "stable",
        variables: { context, language },
      }),
    },
  );
  const { content, version } = await res.json();
  return { content: content as string, version: version as number };
}

export async function reviewCode(code: string) {
  const { content, version } = await fetchLlamaPrompt("code_review", "TypeScript");

  const response = await fetch("http://localhost:11434/v1/chat/completions", {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      model: "llama3.3",
      messages: [
        { role: "system", content },
        { role: "user", content: `Review this TypeScript for bugs:\n\n${code}` },
      ],
    }),
  });

  const { choices } = await response.json();
  console.log({ promptVersion: version });
  return choices[0].message.content as string;
}

Swap http://localhost:11434 for Together, Groq, or your vLLM URL. The PromptForge fetch is identical.

Template:

You are a {{context}} assistant. Respond in {{language}}.
For code review: list issues by severity. Suggest fixes, do not rewrite entire files unless asked.

Version control across model sizes and releases

Llama 8B and Llama 70B need different instruction density. A prompt tuned for 70B often overwhelms 8B or gets ignored.

Llama 3.1, 3.2, and 3.3 respond differently to identical wording. Model upgrades are prompt migration events.

Recommended structure:

Prompt slug	Target	Notes
`llama-8b-classifier`	Small / fast models	Short, explicit instructions
`llama-70b-analyst`	Large models	Detailed rules, examples
`llama-3.3-support`	Specific release	Re-tune when upgrading to 4.x

Each slug has its own version history. Promote independently.

Self-hosted and air-gapped considerations

Self-hosted Llama inference does not require PromptForge to run beside it. Your server needs outbound HTTPS to api.promptforge-app.com (or your self-hosted registry). Inference stays on your network.

For environments without per-request internet access:

Cache prompts in Redis or ElastiCache with a refresh job every 1–5 minutes
Pre-export stable versions to S3 on promotion webhooks
Scheduled sync via Lambda or cron fetching stable channel into internal storage

The pattern is the same as any external config source. PromptForge is not in the inference path except for the fetch.

Llama on Groq vs Llama on Ollama: one registry

Teams often prototype on Ollama and productionize on Groq for speed. Use the same PromptForge prompt ID in both environments. Change only the inference base URL and model string.

If Groq needs shorter instructions for latency-sensitive classification, fork to a separate prompt slug. Do not silently share one prompt across tiers with different capability profiles.

For Groq-specific caching patterns, see Groq prompt management with dynamic templates.

Version channels for open-source production

Same model as hosted providers:

Production: _version=stable (default)
Staging / local dev: _version=latest
A/B or reproduction: pin _version=3

Promote to stable when eval passes. Rollback by re-promoting the previous version. No restart of Ollama or vLLM required if you fetch per request. If you cache, respect TTL or restart cache on promotion.

Full channel guide: Stable vs latest vs pinned.

Open-source LLM ops without prompt sprawl

Without variables, teams clone prompts per locale, persona, or task. Fifty Llama prompts that drift apart.

Use {{language}}, {{task}}, {{tone}} in one template. Fetch with runtime variables. One version history, many outputs.

Logging and debugging

Log promptVersion from the PromptForge response alongside model and inference host. When a self-hosted Llama instance misbehaves, you need to know whether the prompt, the quant, or the runtime changed.

Bind version ID to user-facing outputs in your observability stack. Same discipline as From Playground to Production.

Getting started

Inventory every place a Llama system prompt lives (Ollama Modelfile, env vars, code constants)
Consolidate into one PromptForge template per model tier
Point all inference paths at the same fetch + stable
Use latest on your dev machine
Log version IDs

More examples: Meta / Llama integration page. Foundation: Complete Guide to Prompt Management.