Chaining lets separate AI calls pass work along the line — but each one only sees its own small slice of the problem.

This explores whether prompt chaining is really a workflow pattern where each step sees only part of the picture. The corpus doesn't use the phrase "prompt chaining" much, but it says a lot about the idea under other names. The clearest match is Can algorithms control LLM reasoning better than LLMs alone?, which treats hiding context as the whole point rather than a side effect. An ordinary program controls the order of steps and keeps track of state, and each LLM call gets only what its own sub-task needs. Hiding the rest gets around context-window limits and makes each step easier to debug in isolation. So the answer is yes, and the hiding is a design choice, not an accident.

Other work arrives at the same pattern from different directions. Can reasoning and tool execution be truly decoupled? describes ReWOO, which writes out the full plan before any tools run. Tool results go into placeholders instead of piling up in one growing prompt. This removes the cost of resending an ever-longer history at every step, and it lets independent steps run in parallel. Can models treat long prompts as external code environments? pushes the idea further. The long input never enters the model's view directly. It sits outside, like a file the model queries with code, and the model only sees what it fetches. That approach works on inputs about 100 times larger than the context window, and it beats standard models even on shorter inputs. Across these papers, the workflow decides what each step gets to see.

The less obvious lesson is that hidden context cuts both ways. When steps can't see each other's context, they also can't check each other's work. Can prompts alone reshape multi-agent workflows without system access? shows that a crafted prompt can bias the planning step before any downstream defense looks at it, raising attack success by up to 55%. Executors receive a plan they have no reason to doubt. Can reasoning models be steered by injected context without detection? finds something similar inside a single model. A harmful plan placed in the context gets restated as the model's own reasoning, and chain-of-thought monitors miss it 25 to 33% of the time. In a chain, whatever one step passes to the next arrives with that same unearned trust.

Hidden context also limits what any single prompt can control. Can prompt alignment alone guarantee agent termination in loops? argues that a step inside a looping workflow can't guarantee the loop will stop. That job belongs to a supervisor outside the loop, which matches the LLM Programs idea that control flow belongs in code, not in prompts. There are practical costs too. Does dynamic tool discovery break prompt caching benefits? shows that changing the opening of a prompt from step to step breaks prompt caching. Does prompt optimization without inference strategy fail? shows that tuning a prompt in isolation from how it will be run, such as best-of-N sampling or majority voting, leaves up to 50% of the possible gain unused. Optimizing each link on its own doesn't optimize the chain.

The takeaway the question may not have expected: in prompt chaining, the most important design decision is what each step is not allowed to see. The same hiding that keeps prompts small and steps debuggable also creates blind spots, where an early mistake or injected instruction passes through unchecked. A good chain needs both deliberate context boundaries and something outside the chain that can see across them.