Introduction
The recent wave of agentic large language models (LLMs) has persuaded many executives that code can now be generated at the press of a button. Seasoned engineers know better: when used naïvely, an LLM amplifies bad habits as readily as good ones. The following ten recommendations— distilled from practising Software Planet Group engineers to accelerate development without sacrificing maintainability or professional etiquette.
1. Research Thoroughly Before You Write a Line
Begin every project with a structured “pre-research” sprint. We run multiple research-oriented LLMs (ChatGPT Deep Research, Claude, Gemini and others) in parallel, then merge their outputs into a single dossier before engaging the coding agent. This brute-force literature review surfaces relevant papers, GitHub projects and prior art in minutes, saving days of manual trawling .
Case study: A founder exploring two open computer-vision repositories asked the research agents to enumerate all related work. Within an hour he had a 20-page brief spanning algorithms, benchmarks and known failure modes—commodity knowledge that would have taken a human a week to assemble.
2. Produce an Ultra-Detailed Technical Specification
Treat the LLM like a junior contractor: the clearer the brief, the better the result. The speakers advocate writing a painfully specific technical specification (libraries, version constraints, class diagrams, acceptance criteria) and iterating on it with the model. Each human edit is added to the agent’s long-term memory so future briefs improve continuously .
Practical tip: Allocate at least 30 % of the initial project time to crafting and refining the spec. This front-loads thinking and slashes downstream re-work.
3. Treat the LLM as a Translator, Not as the Architect
LLMs excel at mapping an existing design into idiomatic code; they struggle when asked to invent architecture. Junior developers who abdicate architectural thinking to the model produce “crooked” code because they lack an internal mental model of the system . Decide the structure yourself, then let the agent fill in the boilerplate.
4. Insist on a Written Plan First
A simple yet powerful prompt pattern—“First generate a plan, then implement it”—raises quality dramatically. By forcing the agent to externalise its reasoning, you can inspect and correct the outline before any code is emitted .
5. Write the Tests Before the Code
Include test cases inside the prompt. When the model treats tests as hard constraints, it self-corrects early rather than falling into “fix-it loops” after compilation errors emerge. Our developers stress that test-driven prompting is the antidote to flailing “patch-and-pray” sessions.
6. Prototype Freely, but Rewrite for Production
“Vibe-coding” tools such as Firebase Studio or Bolt New let you dictate prototypes from the bathtub and deploy with one click. They are brilliant for proofs-of-concept but unacceptable for long-term maintenance; before release, the codebase must be rewritten, documented and reviewed by humans .
Case study: A Software Planet Group business analyst built a concept prototype verbally in under an hour, yet the engineering team spent two days converting the throwaway script into production-grade modules with tests and CI.
7. Anticipate Runtime and Environmental Failures
Even a flawless spec cannot prevent out-of-memory errors, disk exhaustion or infinite loops on the target machine. These “edge failures” must be budgeted for in monitoring and observability plans .
8. Pin Dependencies and Isolate Environments
Ask the agent to inspect upstream repositories for version conflicts, suggest virtual-environment strategies and raise incompatibility warnings. This works only if dependency management is explicitly demanded in the brief .
9. Decompose Work to the Smallest Viable Units
Break the task into “atomic” subtasks—interfaces, helper functions, micro-services. Fine-grained decomposition makes it harder for the agent to wander off course and easier to localise defects .
10. Code for Your Colleagues, Not Just the Compiler
Shipping fully auto-generated code without peer review is considered rude: future maintainers will curse the inscrutable style. Always clean, document and refactor machine-written modules before merging to main.
Conclusion
Used with professional discipline, LLMs slash the time between concept and working software. The framework above keeps the human firmly “on the loop”: you provide the architectural vision, the agent handles deterministic translation, and both parties collaborate through rigorous specifications, tests and etiquette. In short, treat the LLM as a gifted—but literal-minded—apprentice, and it will repay you with speed, consistency and a competitive edge.
