> ## Documentation Index > Fetch the complete documentation index at: https://docs.dacard.ai/llms.txt > Use this file to discover all available pages before exploring further. # Map your team's lifecycle > Run the lifecycle assessment to read where your team sits in the build process. Lifecycle measures how your team ships, not what you've shipped. Six stages, 36 tasks, 8 operations-stack categories. Run it once at onboarding to baseline; re-run after a major ship or quarterly. # Map your team's lifecycle As an IC, you want to read where your team sits in the build process so you can pick the next practice to adopt and avoid skipping a stage you'll pay for later. ## What the lifecycle assessment reads Where the URL score reads the product, the lifecycle assessment reads your **team's build process**. It tracks progress through 6 stages and 36 tasks. ## The 6 lifecycle stages Define the problem with the right constraints. What can the system actually solve? What are the guardrails? What does "good enough" look like when outputs are probabilistic? Assemble the knowledge layer the system needs to perform: data pipelines, embeddings, retrieval, prompt engineering, context window discipline. Wire up models, agents, and pipelines. Model selection, chaining, tool use, orchestration architecture. Test quality at the speed of inference. Traditional QA doesn't work for probabilistic outputs. This stage runs on eval frameworks, human review loops, and quality benchmarks. Deploy and manage inference cost. Variable cost structures (tokens, GPU time) need active management. Close the feedback loop. Usage data flows back into model improvement, creating the flywheel that separates Compounding teams from Scaling teams. ## Run your assessment Pick a product from your dashboard, open the **Lifecycle** tab. For each of the 36 tasks, mark your team's status: **not started**, **in progress**, or **done**. We also read your tooling across 8 categories: context and knowledge, model and inference, orchestration, eval and quality, deployment, economics, observability, and feedback. We compute completion per stage and surface where your team sits on the lifecycle. Stages aren't strictly sequential. Most teams work several at once. But skipping a stage outright (especially Context and Eval) creates compounding problems that are expensive to fix later. ## Three cross-cutting reads These forces span all 6 stages and shape every decision. ### Token economics The cost of every interaction. Where traditional software has marginal cost approaching zero, capability-rich products have real per-request cost. Managing token economics is a continuous discipline, not a one-time optimization. ### Role fluidity Modern teams blur traditional role boundaries. Engineers write prompts. Designers evaluate model outputs. PMs manage token budgets. The lifecycle read tracks how well your team has adapted. ### Cognitive debt The capability equivalent of technical debt. Shipping without proper evaluation, monitoring, or feedback loops accumulates cognitive debt: models that drift, prompts that break, outputs that degrade quietly. ## Using the results Your lifecycle report shows progress through each stage. * **Done** stages have mature practice. Maintain them. * **Partial** stages have some practice but gaps remain. These are your highest-leverage investments. * **Not started** stages are areas your team hasn't addressed. Prioritize them if they block downstream stages. ### Common patterns | Pattern | What it means | The move | | --------------------------------- | --------------------------------------------------------------- | ----------------------------------------------------------- | | Strong Specify, weak Eval | Your team defines problems well but ships without quality gates | Invest in eval frameworks and human review loops | | Strong Orchestrate, weak Compound | You build fast but don't learn from usage | Close the feedback loop with analytics and model retraining | | Gaps in Context | Capability lacks the knowledge layer to perform | Prioritize RAG, embeddings, or context management | | Weak Economics | Inference costs are unmanaged | Establish token budgets and cost tracking before scaling | ## How lifecycle complements maturity The URL score reads your product's maturity. The lifecycle assessment reads your team's build process. Together they surface the full picture. * **High maturity, low lifecycle:** product is ahead of process. Sustainability risk. * **Low maturity, high lifecycle:** process is mature but hasn't yet produced results. Patience and execution. * **Both high:** aligned and compounding. Team, process, and product all reinforcing each other. ## What's next Ask DAC to build an improvement plan for your weakest stages. Complement lifecycle with a URL-based maturity score. For leaders who want the cross-framework read. Connected tools provide ground-truth signal that lifts both reads.