WFGY/SAMPLE_DELIVERABLE.md

# SAMPLE_DELIVERABLE

Sample structure of a compact WFGY pilot return package.

This page shows what a small WFGY deliverable may look like after a pilot, audit, or structured review.

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).  
For historical context and public proof, see [EVIDENCE_TIMELINE.md](./EVIDENCE_TIMELINE.md).

---

## What this page is

This page is a sample return package.

Its purpose is to answer a simple but important question:

**what would a team actually receive after a small WFGY pilot**

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.

This page is not:

* 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 sample

Read this page as a sample in three senses at once:

### 1. Structure sample

It shows the sections a compact WFGY return package is likely to include.

### 2. Decision sample

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.

---

# Sample WFGY Return Package

## 1. Engagement snapshot

**Project type**  
RAG or agent workflow review

**Pilot type**  
Failure audit pilot

**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 continue to produce wrong, unstable, or weakly grounded outputs even when the infrastructure appears mostly healthy

**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

The following materials were reviewed in this sample scenario:

* representative failing examples
* selected logs, traces, screenshots, or prompt chains
* a short description of the current architecture
* the team’s current explanations or debugging hypotheses
* key constraints on ownership, tooling, and deployment

### 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

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.

The team reports the following recurring pattern:

* 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

### 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

Before any deeper interpretation, the visible failure surface in this sample case looks like this:

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.

### Observational status

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 return package.

### Primary category cluster

#### A. Retrieval-selection instability

Relevant material is not being surfaced consistently enough across similar requests.

**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

#### D. Evaluation blind spots

The current review loop may detect bad outcomes, but does not yet separate retrieval, orchestration, and answer-layer failures reliably enough.

**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 moves from classification toward deeper reading.

### Highest-probability layers

#### 1. Retrieval and selection layer

**Priority**  
Highest

**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

#### 4. Memory or carryover behavior

**Priority**  
Medium

**Confidence**  
Low to medium

#### 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. Working diagnosis

### Core reading

The current pattern does not look like a pure model-quality problem.

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.

### Why this matters

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 should be concrete, limited, and sequenced.

### Priority 1

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, 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 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

Standardize a short internal vocabulary for repeated failure classes.

**Goal**  
Reduce repeated ambiguity in triage conversations.

**Why fourth**  
This makes future failures cheaper to discuss and faster to route.

---

## 9. What remains uncertain

A good deliverable should say clearly what it does not yet know.

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.

### Still uncertain

1. Whether ranking logic or chunking logic is the dominant upstream driver
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

### 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. Boundaries and non-claims

A compact WFGY return package should clearly state what it does **not** establish.

This sample does not claim:

* that every major failure has been found
* that all root causes are proven
* 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 and more practical:

to improve structural clarity, reduce debugging ambiguity, and make the next round of decisions more disciplined.

---

## 11. Possible follow-on outputs

Depending on scope, a future engagement may extend into outputs such as:

* a cleaner internal failure taxonomy
* a triage worksheet for recurring incidents
* a review rubric for future runs
* a routing guide for common failure types
* a summary note for decision-makers
* a deeper design-partner or integration proposal

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

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 role of a WFGY deliverable at its best.

It helps a team move from:

**something is wrong**

toward:

**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.

---

## Related pages

* [PILOT_OFFER_ONE_PAGER.md](./PILOT_OFFER_ONE_PAGER.md)
* [WORK_WITH_WFGY.md](./WORK_WITH_WFGY.md)
* [CASE_EVIDENCE.md](./CASE_EVIDENCE.md)
* [ADOPTERS.md](./ADOPTERS.md)
* [EVIDENCE_TIMELINE.md](./EVIDENCE_TIMELINE.md)

---

Maintained as a sample structure, not a fixed contract.