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Project Development Methodology guides teams through deciding whether an LLM belongs in a project, validating that choice with a manual prototype, designing a staged architecture, and iterating on implementation. It applies to batch pipelines, multi-agent research systems, and interactive agent applications.

The fit assessment looks for synthesis across sources, subjective judgment with a rubric, natural-language output, tolerance for individual errors, independent batch items, or relevant domain knowledge. It warns against LLM use when work requires precise computation, real-time response, perfect accuracy, proprietary context the model lacks, strongly sequential dependencies, or deterministic output. Manual prototyping should precede coding: use one representative input, assess quality and format, identify failure modes, and establish a baseline. If the prototype fails, automating the same approach is unlikely to help.

Use this skill when starting an LLM project, choosing between agents and traditional code, designing an LLM application, planning a structured batch pipeline, or estimating cost and timelines. It also helps teams decide between a single-agent design and a multi-agent system.

Do not use it to justify adding an LLM to a task simply because an agent can attempt it. Exact algorithms, strict deterministic requirements, and latency-sensitive computation are identified as poor fits. Proprietary-data dependence and sequential coupling also need careful evaluation. The skill recommends adding architectural complexity only when the project demonstrates a need for it.

Start with the project goal, task characteristics, desired output, acceptable error rate, value per successful result, available data, and constraints. A manual prototype tests whether the target model can supply the needed knowledge and format, shows expected quality, and exposes likely failures. The planning template then considers required tools and data sources, storage, testing, parallelization, cost, infrastructure, and ongoing work.

For pipelines, use discrete, idempotent, cacheable, independent stages. The canonical flow is acquire, prepare, process, parse, and render. Acquisition fetches data from APIs, files, or databases; preparation turns it into prompt input; processing performs the expensive, non-deterministic model call; parsing extracts structure; rendering produces reports, files, or visualizations. Because the other stages are deterministic, this separation can let a team revise parsing or rendering without repeating model work.

A file system can hold each item's raw input, prompt, response, and parsed result in its own directory. File existence marks completed stages, supports inspection and debugging, and lets a team rerun a stage by removing its output and downstream results. The source presents this as an alternative to databases or in-memory state for tracking a pipeline.

Structured-output prompts should define section markers, exact formats, examples, rationale for parsing, and constrained values. Parsing should tolerate minor formatting variation, use sensible defaults when sections are absent, and log failures rather than crashing. A prompt example in the source demonstrates the structure:

Analyze the following and provide your response in exactly this format:

[Your summary here]

Rating: [1-10]

• Key point 1
• Key point 2

Follow this format exactly because I will be parsing it programmatically.

Cost estimates can use item count, tokens per item, price per token, and API overhead, with a 20-30% buffer for retries and failures. Track actual spending and reconsider context length, model size, caching, or call scheduling if costs exceed estimates. Single-agent architecture suits independent work and simpler operations; multi-agent architecture may fit parallel exploration or tasks needing isolated context. Context isolation, rather than role labels, is the stated reason to add specialized agents.

Product and engineering teams planning an LLM system can use the methodology to test task fit, keep pipeline stages manageable, and adapt architecture based on evidence. It emphasizes generating an initial implementation, testing specific failures, refining prompts and architecture, and repeating that cycle while avoiding monolithic pipelines, skipped validation, ignored costs, rigid parsers, and premature optimization.