Everyone is talking about what LLMs can do, but the real challenge is getting them to do what you want. Unpredictable, rambling, or incorrect outputs are common. The key isn't just a better prompt; it's about designing a system of constraints around the model.
Here are eight practical techniques to steer LLM behavior and get more reliable results.
1. RAG (Retrieval-Augmented Generation)
Fetch external bits of information (like documents or web search results) and inject them directly into the prompt. This grounds the model's answers in verifiable facts, which is excellent for reducing hallucinations and providing up-to-date responses.
2. Database as Context
Treat your retrieved data like a local context store. You can index it, break it into chunks, and feed it into the prompt based on relevance and freshness rules. This gives the model a working memory tailored to the task.
3. Tool Calling
Instead of just replying with text, give the LLM a menu of actions it can perform. These can be functions like getWeather(location) or createCalendarEvent(title, date, time). The model can then choose to execute these tools to gather information or perform tasks, making it an active participant rather than a passive generator.
4. Structured Outputs
Force the model's output into a specific shape, like JSON or CSV. For example, you can instruct it to always return {"event_name": string, "attendees": [string], "is_virtual": boolean}. This makes the results parsable and machine-friendly, perfect for API integrations.
5. Workflows
Break down large, complex jobs into an ordered sequence of smaller LLM calls (e.g., step 1 → step 2 → step 3). This makes the overall process more deterministic and provides explicit stopping points for verification and debugging.
6. Agentic Loops
This is where the LLM becomes more autonomous. It uses tools, reads the results, updates its own prompt or internal state, and repeats the cycle until a goal is met. This is powerful but requires careful guardrails, such as a maximum number of steps and a list of safe, permitted actions.
7. APIs
Wrap the model's capabilities as typed endpoints that it can call. This makes its behavior more predictable, auditable, and easier to integrate into larger software systems. It turns the LLM from a black box into a service.
8. Reasoning
Push the model to "think" through a problem before giving an answer. Techniques like Chain-of-Thought (CoT) or using <think> tags encourage the model to perform multi-step logic. However, you should only expose the final output and ensure you can safely parse and verify it.
All of these are variations on the same fundamental skill: designing the prompt and the constraints so the model behaves exactly like you want.