From Prompt to Context Engineering: A 6-Element Framework for Building Apps with AI
Prompt engineering had a useful run. It taught a generation of professionals how to ask a language model a better question. That skill remains worth having. It is no longer the bottleneck for building software with AI.
The bottleneck now is context engineering. The distinction is real and the mental shift is bigger than it sounds. This article explains the difference and lays out a 6-element framework you can paste directly into Lovable, Cursor, Bolt or any AI builder you use.
What changed: prompt engineering versus context engineering
Prompt engineering is the practice of phrasing a single instruction to an LLM well. You write “summarise this email in three bullet points, use a polite tone, keep each bullet under twelve words.” You iterate on phrasing until the output stabilises.
Context engineering is the practice of giving an AI system all the surrounding information it needs to behave correctly across many prompts. You define what the system is for, who uses it, what data it sees, what rules it must follow, and what success looks like. The individual prompts become small. The brief around them becomes substantial.
The shift is identical to the shift from working alone to managing your first hire. You stop optimising the request and start optimising the conditions under which the request makes sense. Industry Rockstar’s teaching session put it bluntly: when we move from single LLM calls to actual agents and app builders, we are out of prompt engineering and into context engineering.
The 6-element framework
A practical framework, in the order an engineer or developer would expect to receive a brief:
- Context. What is this for, who will use it, and how does it fit into the rest of the operation. Internal tool or external product. Sales team or marketing team. One-off experiment or part of a hub.
- Object. The specific outcome the system is built to produce. Qualify leads before booking sales calls. Score grant applications by readiness. Render personalised landing pages for paid ad campaigns.
- Input. What information enters the system. Name, email, budget, timeline, project type. A CSV of ad keywords. A transcript file.
- Logic. What happens to those inputs. Score by budget and timeline. Match against a knowledge base. Route to one of three follow-up flows.
- Output. What the system produces and where the result goes. Display a score, send a webhook to Make, file a row in a CRM, generate a PDF.
- Constraints. The limits the system must respect. Internal use only. No PII written to logs. Keep version one minimal. Budget cap per user.
A worked example, end to end
Here is the same example, expanded into a single brief you could paste into Lovable on Monday morning:
Context. Internal tool for the Vimaxus sales team. Used before each discovery call to filter out unqualified leads.
Object. Qualify inbound leads on budget and timeline so the sales team only meets with leads that pass a minimum threshold.
Input. Name, email, phone, budget range (4 brackets), timeline (3 brackets), project type (multi-select from 6 options).
Logic. Score = budget_points (0 to 40) + timeline_points (0 to 30) + project_type_match (0 to 30). Pass threshold = 60. Below 60 = nurture sequence, above = book call.
Output. Display score to the lead as “Recommended next step.” Send webhook to Make with full payload + score. Make routes to either Calendly booking link or nurture sequence in HubSpot.
Constraints. Internal use only for first 30 days. No external sharing. Keep version one minimal: one page, one form, one result screen. No login. No analytics yet.
Paste that into Lovable. You will get a working lead qualifier in a single session. More importantly: the platform now understands enough about your operation that the inevitable follow-up prompts (“add a French version,” “swap Calendly for Cal.com,” “export weekly to CSV”) land cleanly. The first long brief saves dozens of small clarifying prompts later.
Knowledge base: context that survives the session
The six elements above describe one project’s context. Operators running multiple AI-built tools need a layer above that: a persistent knowledge base that travels from project to project.
In practice this is a small folder of Markdown files: a brand guide, a tone of voice document, a list of preferred integrations, a design system note, a copy of your CRM schema. Upload these to every new project before the first prompt. Both Lovable and Cursor accept knowledge files as project attachments. The output is more consistent, the iteration speed is higher, and a teammate using the same knowledge base produces work that fits next to yours without retraining the AI.
A practical starter pack: 5 to 8 Markdown files, each under 2,000 words. Brand voice, visual design rules, business glossary, integration list, escalation paths. That is enough to give the AI a working sense of who you are.
Where this breaks
Three failure modes are worth naming so you can spot them in your own work:
- Context that contradicts itself. Your brand guide says “casual, warm” and your design system says “corporate, minimal.” The AI averages the two and produces something neither side recognises. Audit your knowledge base for internal contradictions before uploading.
- Constraints that are too vague. “Keep it simple” means nothing to a code generator. “One page, no auth, no database, deploy as static HTML” means something. Constraints should be testable.
- Inputs that the system cannot actually receive. Asking the system to use the user’s company size when you never collect company size sets up a confused implementation. The Input element forces honesty about what data you actually have.
The shift in one sentence
Prompt engineering optimises the question. Context engineering optimises the brief. Most of the time saved when AI builds your apps comes from getting the brief right before the AI starts typing.
Article co-written by Viktoriia Didur and Elis. Framework attributed to Industry Rockstar teaching session, 2026.
