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symptom-layer

4 min read

The Symptom Layer Discovery

Map the gap between what you sell and what your clients actually experience before they know they need help — surfacing pre-awareness symptoms, client-language queries, and content blind spots. From Dirk’s ontology breakthrough and Don Back’s insight on pre-recognition search behavior (Jan 15, 2026).

You will map the ontological gap between an expert’s offering and the lived experience of their ideal client BEFORE that client recognizes they need help. The output must be grounded in how real people describe problems to friends, search engines, and AI assistants — not in professional terminology.

MY EXPERTISE: $ARGUMENTS

If no expertise was provided above, ask me to describe it before proceeding.

MY IDEAL CLIENT: [BRIEF DESCRIPTION — e.g., mid-career professionals feeling stuck, CEOs with team instability. Say “you decide” to have me infer from your expertise description]

If any parameter says “you decide,” infer the most reasonable value, state it, and proceed. I’ll correct if needed.

STEP 0 — PERSPECTIVE SELECTION: Based on the expertise described, adopt the perspective combination most useful for this analysis. This must include at minimum:

  • The CLIENT’s inner monologue (what they’re feeling and searching, in their words)
  • An ONTOLOGY ANALYST mapping the structural gap between expert language and client experience

Additionally, select any perspectives revealed by the specific expertise — including but not limited to: the client’s manager or partner who observes the symptoms externally, adjacent professionals the client might consult first (e.g., a therapist before a career coach, a CPA before a business strategist), or the AI assistant the client would query before ever finding you.

STEP 1 — ROOT CAUSE MAPPING: Identify the 3-5 root cause problems your expertise actually solves — not the service you deliver, but the underlying dysfunction. For each, distinguish between:

  • The STRUCTURAL root cause (the system-level problem)
  • The EXPERIENTIAL root cause (what it feels like to live inside that problem)

STEP 2 — SYMPTOM EXCAVATION: For each root cause, map 3-5 symptoms the client experiences BEFORE they know they need you. These must be:

  • Observable (something they or others around them can point to)
  • Felt (an emotional or practical pain, not an abstract concept)
  • Pre-diagnostic (phrased without any of your professional vocabulary)

The test: would this person describe this symptom to a friend over drinks? If not, you’re still in expert language.

STEP 3 — CLIENT-LANGUAGE QUERIES: For each symptom, generate 3 queries the client might actually type — into Google, into ChatGPT, into Perplexity. These should range from:

  • Vague/emotional (“why do I dread going to work”)
  • Specific/situational (“how to deal with a boss who takes credit for my ideas”)
  • AI-conversational (“I feel stuck in my career but I don’t know what I actually want — can you help me figure it out”)

STEP 4 — CONTENT GAP ANALYSIS: For each symptom-query pair, assess: does the expert currently have ANY content that would surface for this query? Mark as COVERED, PARTIALLY COVERED, or BLIND SPOT.

BLIND SPOTS are the high-value output — these are visibility gaps where ideal clients are actively searching and the expert is invisible.

STEP 5 — VERIFICATION:

  • Are the symptoms genuinely pre-awareness, or am I describing problems the client would only articulate AFTER finding the expert? Re-check each.
  • Are the queries realistic things a human would type, or are they keyword-stuffed search terms? Read each aloud — if it sounds robotic, rewrite it.
  • Am I projecting assumed client psychology, or grounding in observable behavior? Flag any symptom where confidence is below 75%.

OUTPUT FORMAT: Deliver as a table: Root Cause → Symptom → Client Language Query (x3) → Content Gap Status

Then: a prioritized list of the top 5 blind spots, ranked by: (a) how many potential clients are likely searching for this, and (b) how directly it connects to the expert’s offering. For each, suggest a specific content piece (article title, FAQ, or page section) that would close the gap.

Source

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