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# SAMPLE_DELIVERABLE
Sample shape of a compact WFGY pilot return package.
Sample structure of a compact WFGY pilot return package.
This page shows what a small WFGY deliverable may look like in practice.
This page shows what a small WFGY deliverable may look like after a pilot, audit, or structured review.
It is not a universal template for every engagement.
It is a sample structure that makes the output shape legible before any formal collaboration begins.
It is not a promise that every engagement will produce the same sections in the same length.
It is a practical sample that makes the expected output shape easier to understand before collaboration begins.
For the pilot entry itself, see [PILOT_OFFER_ONE_PAGER.md](./PILOT_OFFER_ONE_PAGER.md).
For the broader collaboration entry, see [WORK_WITH_WFGY.md](./WORK_WITH_WFGY.md).
@ -15,39 +15,46 @@ For historical context and public proof, see [EVIDENCE_TIMELINE.md](./EVIDENCE_T
## What this page is
This page is a sample output format.
This page is a sample return package.
Its purpose is simple:
Its purpose is to answer a simple but important question:
show what a compact WFGY return package may contain after a small pilot, audit, or structured review.
**what would a team actually receive after a small WFGY pilot**
This page is here to answer practical questions such as:
The answer is not “a vague summary.”
The intended shape is a bounded, structured, decision-useful package that helps a team move from scattered symptoms toward clearer categories, clearer boundaries, and more disciplined next steps.
1. What would a team actually receive
2. How concrete would the analysis be
3. What kind of structure would WFGY impose on messy failures
4. What is included, and what is not
This page is not:
This is not a legal contract, not a guarantee of outcomes, and not a fixed statement of scope for every future collaboration.
* a fixed legal scope
* a formal statement of work
* a guarantee of outcome
* a claim that every engagement will uncover the same level of clarity
It is a model of what “useful structure” can look like.
---
## Best way to read this page
## Best way to read this sample
The sample below should be read as:
Read this page as a sample in three senses at once:
1. a shape example
2. a structure example
3. a boundary example
### 1. Structure sample
It is not meant to imply that every real engagement will have the same length, same inputs, or same number of findings.
It shows the sections a compact WFGY return package is likely to include.
The main point is to show that WFGY outputs are intended to be:
### 2. Decision sample
* structured
* bounded
* readable by mixed teams
* useful for next-step decisions
It shows the kind of judgments WFGY tries to make legible:
* what the likely failure layers are
* what is high confidence versus low confidence
* what next moves are worth doing first
### 3. Boundary sample
It shows that a good deliverable should not only say what seems likely.
It should also say what remains uncertain and what is out of scope.
---
@ -61,203 +68,310 @@ RAG or agent workflow review
**Pilot type**
Failure audit pilot
**Review window**
Small scoped pilot based on a limited batch of representative cases
**Review scope**
Small scoped review based on a limited set of representative failures, system notes, and current debugging assumptions
**Primary question**
Why does the system keep producing wrong or unstable outputs even when the infrastructure appears mostly healthy
Why does the system continue to produce wrong, unstable, or weakly grounded outputs even when the infrastructure appears mostly healthy
**Output goal**
Turn scattered symptoms into a smaller set of structured failure categories, identify likely root-cause layers, and propose a practical next-step sequence
**Deliverable goal**
Convert scattered symptoms into a smaller set of structured categories, identify the most likely failure layers, and recommend a practical next-step sequence
**Overall reading**
The system does not appear to be facing one isolated issue.
The evidence suggests a layered debugging problem with multiple interacting surfaces.
---
## 2. Inputs reviewed
A compact WFGY pilot may review material such as:
The following materials were reviewed in this sample scenario:
* representative failing cases
* representative failing examples
* selected logs, traces, screenshots, or prompt chains
* architecture notes or system sketches
* current debugging hypotheses from the team
* known constraints on tooling, ownership, or deployment
* a short description of the current architecture
* the team’s current explanations or debugging hypotheses
* key constraints on ownership, tooling, and deployment
The exact input set may vary.
The point is not to ingest everything.
The point is to review enough evidence to form a disciplined structural reading.
### Boundary note
The pilot does not assume full access to every production component.
The goal is to review enough material to reach a disciplined structural reading, not to claim omniscience over the whole system.
---
## 3. System snapshot
This section gives a short plain-language description of the system under review.
The reviewed system is a retrieval-backed generation workflow with a multi-step prompt construction path, a document retrieval layer, a ranking layer, and a final answer-generation stage.
Example:
The team reports the following recurring pattern:
The reviewed system is a retrieval-backed generation workflow with a multi-step prompt assembly path, a document store, a ranking layer, and a final answer-generation stage. The team reports recurring answer drift, occasional confident hallucinations, and unstable behavior across similar queries.
* some answers are fluent but incorrect
* similar questions may produce different retrieved evidence and different final outputs
* some failures appear before final generation
* debugging discussions often collapse multiple error types into one generic label
This section should be short.
Its job is to establish context, not rewrite the team’s internal docs.
### Why this section exists
This section is intentionally short.
Its purpose is to establish a readable shared context before moving into classification and judgment.
---
## 4. Observed failure surface
This section lists the visible symptoms before deeper classification.
Before any deeper interpretation, the visible failure surface in this sample case looks like this:
Example:
1. The system often produces answers that sound stable but are not reliably grounded in the retrieved material.
2. Similar inputs do not consistently lead to similar retrieval and answer behavior.
3. Evidence suggests that some failures emerge before final answer generation, especially in selection or context preparation.
4. The current debugging loop appears to focus heavily on the model output itself, while upstream layers may be contributing materially to the final result.
1. Answers are often plausible in tone but wrong in content
2. Similar queries produce inconsistent retrieval and inconsistent final answers
3. Some failures appear to begin before generation, especially in retrieval selection or context assembly
4. The team’s current debugging process focuses heavily on model behavior, but evidence suggests multiple upstream layers may be involved
### Observational status
This section stays close to what was actually observed.
This section stays close to visible behavior.
It does not yet claim root cause.
---
## 5. Structured failure classification
This is one of the core sections in a WFGY-style deliverable.
Example:
This is one of the core sections in a WFGY-style return package.
### Primary category cluster
1. Retrieval-selection instability
The system appears to surface context that is variably relevant across similar requests.
#### A. Retrieval-selection instability
2. Context assembly distortion
Even when useful material exists, the assembled context may over-compress, fragment, or misweight it before answer generation.
Relevant material is not being surfaced consistently enough across similar requests.
3. Final-answer overconfidence
The generation layer sometimes presents uncertain or weakly grounded outputs in a form that looks more stable than the evidence supports.
**Confidence**
High
**Why this appears likely**
Repeated variation in retrieved context suggests the issue begins upstream of final generation in at least part of the case set.
#### B. Context assembly distortion
Useful material may exist, but the way it is combined, compressed, ordered, or weighted may reduce its practical usefulness before generation.
**Confidence**
Medium to high
**Why this appears likely**
Some failures show a gap between the presence of relevant source material and the quality of the final answer.
#### C. Final-answer overconfidence
The answer layer sometimes presents weakly supported outputs with stronger confidence than the evidence can justify.
**Confidence**
Medium
**Why this appears likely**
Observed outputs appear rhetorically stable even when grounding is partial or inconsistent.
### Secondary category cluster
1. Evaluation blind spots
The current review loop may be catching obvious wrong answers, but not consistently separating retrieval failure from orchestration failure.
#### D. Evaluation blind spots
2. Triage vocabulary weakness
The team may be discussing several different failure layers under one generic label, making debugging slower and less reproducible.
The current review loop may detect bad outcomes, but does not yet separate retrieval, orchestration, and answer-layer failures reliably enough.
This section is where WFGY becomes useful.
Its role is to convert raw symptoms into a smaller set of meaningful buckets.
**Confidence**
High
#### E. Triage vocabulary weakness
Multiple distinct failure patterns may be grouped under one generic description, making debugging slower and less reproducible.
**Confidence**
High
### Why this section matters
This section converts raw symptoms into smaller, reusable buckets.
That matters because teams often lose time not only from technical issues, but from category confusion.
---
## 6. Likely root-cause layers
This section gives a disciplined reading of where the problem most likely lives.
Example:
This section moves from classification toward deeper reading.
### Highest-probability layers
1. Retrieval and selection layer
Evidence suggests that at least part of the failure begins before the model writes the answer.
#### 1. Retrieval and selection layer
2. Context construction layer
The prompt may be receiving material that is technically relevant but structurally misassembled.
**Priority**
Highest
3. Review and evaluation layer
The team’s current debugging loop may not yet isolate layer-specific failure signatures well enough.
**Confidence**
High
**Reading**
At least part of the observed failure surface likely begins before the model writes the final answer.
#### 2. Context construction layer
**Priority**
High
**Confidence**
Medium to high
**Reading**
The prompt may be receiving technically relevant material in a structurally degraded form.
#### 3. Review and evaluation layer
**Priority**
High
**Confidence**
High
**Reading**
The current internal debugging loop may not yet distinguish failure signatures by layer clearly enough to support fast iteration.
### Lower-confidence but relevant layers
1. Memory or carryover behavior
2. Tool or handoff instability
3. Prompt framing side effects
#### 4. Memory or carryover behavior
This section should distinguish between:
**Priority**
Medium
* likely
* possible
* still unclear
**Confidence**
Low to medium
That distinction matters a lot.
#### 5. Tool or handoff instability
**Priority**
Medium
**Confidence**
Low to medium
#### 6. Prompt framing side effects
**Priority**
Medium
**Confidence**
Low
### Interpretation rule
A strong deliverable should separate:
* what appears likely
* what remains possible
* what is still too weak to assert
That distinction is part of the value.
---
## 7. What this most likely means
## 7. Working diagnosis
This section translates the diagnosis into practical reading.
### Core reading
Example:
The current pattern does not look like a pure model-quality problem.
The current pattern does not look like a pure model-quality problem.
It looks more like a layered systems problem in which retrieval quality, context assembly, and evaluation framing interact to produce unstable final answers.
The stronger reading is that this is a layered systems problem in which retrieval quality, context assembly, and evaluation framing interact to produce unstable or weakly grounded final answers.
This matters because the team may waste time if it continues to treat the issue only as “the model hallucinated.”
The evidence suggests the debugging route should become more layered and more explicit.
### Why this matters
This section is not supposed to sound dramatic.
It is supposed to sharpen decision quality.
If the team continues to read the issue only as “the model hallucinated,” it may keep applying fixes at the wrong layer.
The evidence in this sample case suggests that the more useful route is to separate the failure surface into upstream selection, context construction, and final-answer expression.
### Boundary
This is a working diagnosis, not a claim of full proof.
---
## 8. Recommended next moves
This section is the most actionable part of the package.
Example:
This section should be concrete, limited, and sequenced.
### Priority 1
Separate retrieval failure from generation failure using a smaller reviewed case set.
Do not treat all incorrect answers as one category.
Separate retrieval failure from generation failure using a smaller reviewed case set.
**Goal**
Stop treating all bad answers as one category.
**Why first**
This creates the cleanest structural gain for the least cost.
### Priority 2
Inspect context assembly rules for compression, ranking, and ordering artifacts.
Check whether relevant material is being technically retrieved but practically neutralized before generation.
Inspect context assembly rules for compression, ranking, truncation, and ordering artifacts.
**Goal**
Check whether useful material is being technically retrieved but practically neutralized before generation.
**Why second**
This is one of the most likely places where “good inputs turn into weak answer conditions.”
### Priority 3
Add a lightweight review frame that tags each failure by likely layer before discussing fixes.
Add a lightweight layer tag to internal review.
**Goal**
Mark each failure as most likely retrieval, assembly, answer, tool, memory, or evaluation related before discussing fixes.
**Why third**
A small tagging habit often improves debugging clarity more than another round of vague brainstorming.
### Priority 4
Use a shared internal vocabulary for repeated failure categories so that future triage is faster and less dependent on individual intuition.
Standardize a short internal vocabulary for repeated failure classes.
These actions should be concrete, limited, and realistically sequenced.
**Goal**
Reduce repeated ambiguity in triage conversations.
**Why fourth**
This makes future failures cheaper to discuss and faster to route.
---
## 9. What is still uncertain
## 9. What remains uncertain
This section is very important.
A good deliverable should say clearly what it does not yet know.
Example:
In this sample scenario, the reviewed material is sufficient for a structured preliminary reading, but not sufficient for strong claims about all long-run production behavior.
The reviewed material is enough to support a structured preliminary diagnosis, but not enough to make strong claims about long-run production behavior, security properties, or full-system robustness under all workloads.
The following remain uncertain:
### Still uncertain
1. Whether ranking logic or chunking logic is the dominant upstream driver
2. Whether memory effects are meaningful or incidental
3. Whether some failures are benchmark-specific rather than architecture-level
2. Whether carryover or memory effects are meaningful or only incidental
3. Whether some observed failures are benchmark-specific rather than architecture-level
4. Whether the same pattern holds consistently across all major workload classes
A good deliverable should say what it does not yet know.
### Why this section matters
Without an uncertainty section, teams often over-read a pilot and treat it as a full-system verdict.
That would be a mistake.
---
## 10. What this does not claim
## 10. Boundaries and non-claims
A sample WFGY return package should clearly state its boundaries.
A compact WFGY return package should clearly state what it does **not** establish.
Example:
This review does not claim:
This sample does not claim:
* that every major failure has been found
* that all root causes are proven
* that the system is close to production readiness
* that architectural changes are unnecessary
* that a small pilot replaces engineering, security, or infra work
* that the system is near production readiness
* that architecture changes are unnecessary
* that a small pilot replaces engineering, security, or infrastructure work
* that every future failure will fit the same categories
The purpose of the package is narrower:
The purpose of the package is narrower and more practical:
to improve structural clarity, reduce debugging ambiguity, and make the next round of decisions more disciplined.
@ -265,34 +379,38 @@ to improve structural clarity, reduce debugging ambiguity, and make the next rou
## 11. Possible follow-on outputs
Depending on scope, a future engagement might extend into outputs such as:
Depending on scope, a future engagement may extend into outputs such as:
* a cleaner failure taxonomy for the team
* a triage protocol based on recurring patterns
* a lightweight debug worksheet
* a cleaner internal failure taxonomy
* a triage worksheet for recurring incidents
* a review rubric for future runs
* a pilot summary for decision-makers
* a deeper integration or design-partner proposal
* a routing guide for common failure types
* a summary note for decision-makers
* a deeper design-partner or integration proposal
Not every pilot needs these.
They are possible extensions, not default promises.
These are possible extensions.
They are not automatic promises.
For the pilot framing that may lead into these, see [PILOT_OFFER_ONE_PAGER.md](./PILOT_OFFER_ONE_PAGER.md).
---
## 12. Why this sample matters
This sample matters because many teams do not need more vague advice.
They need a clearer way to turn messy failures into smaller, more meaningful decisions.
Many teams do not need more generic advice.
They need a better way to move from messy evidence to smaller, more meaningful decisions.
That is the main role of a WFGY deliverable.
That is the role of a WFGY deliverable at its best.
At its best, it helps a team move from:
It helps a team move from:
“something is wrong”
**something is wrong**
to:
toward:
“these are the likely failure layers, these are the boundaries, and these are the next moves worth trying.”
**these are the likely failure layers, these are the boundaries, and these are the next moves worth trying**
That is a much better place to be.
---