Prompt Composition in Practice

The techniques and components covered in the preceding chapters do not exist in isolation. Expert prompt engineering involves combining them deliberately, managing the constraints of the context window, and building prompt structures that are reusable and maintainable. This chapter addresses the practical craft of putting it all together.

Layering Techniques

Most real-world prompts benefit from combining multiple techniques. A prompt that assigns a role, provides context, supplies few-shot examples, requests chain-of-thought reasoning, and specifies an output format is not overcomplicated. It is precisely configured.

The key discipline is ordering. A prompt that buries the task instruction after several paragraphs of context and examples will perform worse than one that leads with the instruction, because the model's attention to the instruction is highest when it appears early. A useful default ordering is:

1. Role definition (if not set in the system prompt)

2. Task instruction

3. Output specification

4. Context

5. Few-shot examples (if used)

6. Input data

7. Any closing reinforcement of key constraints

This ordering places the most important directive elements early, where they receive maximum attention weight, and places the input data last, immediately before the model begins generating, which keeps it maximally fresh in the model's context.

Prompt Templates and Reusability

For tasks you perform repeatedly, a prompt template is a prompt with variable placeholders that you fill in at query time. Templates encode the stable, reusable components of a prompt (task instruction, output specification, constraints, few-shot examples) and separate them from the variable components (input data, context specific to the current instance).

A well-designed template reduces the per-query cognitive overhead of prompt construction, ensures consistency across repeated uses of the same task, and makes it easier to isolate and improve individual components without rebuilding the entire prompt.

In GLBNXT Workspace, prompt templates can be stored and retrieved through the interface, making them accessible across sessions without manual re-entry. Building a library of tested, versioned templates for your most common tasks is one of the highest-leverage investments an experienced Workspace user can make.

Context Window Management

The context window is finite, and managing it well becomes increasingly important as prompts grow in complexity and conversation histories grow in length. Several principles govern effective context window management.

Front-load critical instructions. Content at the beginning of the context window has stronger influence on generation than content in the middle. If the context window is partially full of conversation history, and your new instruction is appended at the end, its influence is weaker than if it were at the start.

Trim redundant context. Every token of context that does not contribute to the current task is a token that could have been used for something that does. In long conversations, periodically summarising prior context rather than carrying the full history reduces context window pressure without losing essential information.

Be selective about retrieved documents. In RAG-enabled environments, retrieved document chunks consume significant context window capacity. Prompts that instruct the retrieval system to surface only the most relevant passages, rather than maximising retrieval volume, preserve capacity for the task instruction and output generation.

Know your model's effective context length. Many models nominally support large context windows but exhibit degraded performance on content in the middle of very long contexts, a phenomenon documented in research as the "lost in the middle" effect (Liu et al., 2023). For tasks where the position of input data within the context is variable, placing the most critical information at the beginning or end of the context window is more reliable than placing it in the middle.

Iterative Refinement as a Practice

Prompt engineering is not a one-shot activity. The first version of a prompt is a hypothesis. The response it produces is evidence. Systematic improvement requires treating each iteration as a diagnostic exercise: what did the response get right, what did it get wrong, and which component of the prompt is responsible for each failure?

A disciplined iteration process works as follows. Run the prompt against a representative test set of inputs. For each failure, identify the failure mode from the taxonomy in Chapter 4. Modify the specific prompt component responsible for that failure. Rerun the full test set, not just the case that prompted the modification. Confirm that the change improved performance on the failing case without degrading performance on cases that were previously passing.

This process is slower than ad hoc tweaking, but it produces prompts that generalise reliably rather than prompts that work on the cases you tested and fail on the ones you did not.