When we ask a language model to do a task, we often send one prompt and wait for one answer. Sometimes that is fine. But for harder work like coding, research, or long writing, this can fail or give random results.
A thought process generator is a way to improve this. Instead of going straight to the answer, we first define how the agent should think. We write down steps, order, checks, and what each step should produce. This turns a vague request into a clear plan.
A good way to define these plans is with a small domain specific language (DSL). The DSL is just a simple way to describe a thought process. For each process we list steps, inputs, outputs, and actions. For example, for a coding task, one process could be: first understand the problem, then plan a solution, then write the code, then check the code. The exact syntax is not important. What matters is that the plan is clear and easy to read and reuse.
For one task there can be many possible thought processes. The generator should be able to create different plans for the same task. These can vary in how many steps they have, how deep they go, and what kind of strategy they use. For a research task, one plan might start broad then go deep. Another might go deep into one source first. A third might write a rough draft first and then fill in gaps. The key idea is to have more than one way to think about the same problem.
Once we have several thought processes, we need to pick one. We can look at how clear each plan is. We can check if it covers all parts of the task. We can see if it is too long or too complex. We can check if it has steps for review and verification. Simple rules can help rank these plans. For example, for tricky tasks we can favor plans that include checks and self-review. For simple tasks we can favor short plans with fewer steps. Then we select the best one and use that to guide the agent.
After we choose a plan, the agent runs it step by step. The output of one step becomes the input to the next. If a step fails or finds missing info, the plan can say what to do: go back to an earlier step, ask the user for more details, or switch to a different path. We can log each step. This makes it easier to see what went wrong and to improve the plan later. It also makes the reasoning more visible and easier to trust.
To avoid designing every thought process from scratch, we can use patterns. A pattern is a common shape of thinking we use often. For example, a research pattern might be: collect sources, pull out key points, compare them, write a summary, then check. A problem solving pattern might be: clarify the problem, split it into smaller parts, solve each part, join the solutions, then review. A writing pattern might be: outline, draft, edit, polish. The generator can start from these patterns, then adjust them to fit the current task.
Many tasks have more than one “thought chain”. A thought chain is a line of thinking for one part of the job. For example, building a small feature might have a chain for understanding the need, a chain for designing the solution, a chain for coding, and a chain for review. Each chain can have its own thought process. The outputs of one chain feed into the next. The generator can create or reuse a process for each chain and link them.
Reuse is important. When a thought process works well for one task, we can save it and use it again on similar work. Over time we can build a small library of processes like “bug fixing”, “feature design”, “topic research”, “blog post writing”. When a new task comes in, we find a close match and adapt that process instead of starting from zero. We can remove or add steps and tune the level of detail. This is similar to how people develop mental workflows and reuse them.
A useful way to think about all this is to compare it to a database execution plan. When you send a query to a database, it does not run it directly. It first creates several possible ways to run the query. It estimates which way is best. Then it picks one plan and executes it. We can do the same with thought processes. The user’s task is like the query. The different thought processes are like different execution plans. The choice of plan is based on simple rules. The final result comes from running that plan step by step.
This kind of thought process generator can help make language model agents more stable and clear. It gives structure to tasks. It lets us see and change how the agent thinks. It allows reuse of good plans from past work. A simple next step is to sketch a small DSL for thought processes in your own domain. Then list a few patterns you use often. Finally, try having an agent generate, rate, and run these processes on real tasks, and refine them over time.
