Inferensys

Prompt

Dependency Version Conflict Resolution Prompt

A practical prompt playbook for using the Dependency Version Conflict Resolution Prompt in production AI-assisted DevOps workflows.
Operations team reviewing AI workflow automation on laptop, workflow builder visible, casual office setup.
PROMPT PLAYBOOK

When to Use This Prompt

Defines the ideal scenario, required inputs, and limitations for the dependency version conflict resolution prompt.

This prompt is designed for developers and platform engineers who need to resolve package manager dependency conflicts automatically. It takes a raw dependency manifest file and the exact conflict error output from a tool like npm, pip, Maven, or Cargo, and proposes a compatible version set. Use this prompt inside a CI failure recovery step, a local developer CLI tool, or an automated dependency update bot.

The prompt works best when the conflict is purely a version resolution problem—where a set of compatible versions exists but the resolver cannot find it without explicit guidance. It requires two concrete inputs: the full dependency manifest (e.g., package.json, requirements.txt, pom.xml, or Cargo.toml) and the unmodified error text from the package manager. Without both, the model lacks the constraint surface needed to propose a working version set. The output should be a structured version override block that can be applied directly to the manifest or lock file, accompanied by a brief explanation of which constraints were relaxed or pinned to achieve resolution.

This prompt is not a replacement for a full dependency resolver or a human architect when the conflict stems from a fundamental API incompatibility that requires code changes. If a package has removed a function your code calls, no version pinning will fix it. Similarly, do not use this prompt for security vulnerability triage—it does not assess CVE risk, only compatibility. For high-risk production deployments, always pair the prompt's output with a lock file regeneration test and a human review step before merging.

PRACTICAL GUARDRAILS

Use Case Fit

Where the Dependency Version Conflict Resolution Prompt works, when it fails, and the operational risks to manage before wiring it into a CI pipeline or developer tool.

01

Good Fit: Deterministic Package Managers

Use when: The conflict is surfaced by a deterministic resolver (npm, pip, Cargo, Maven) with a complete error trace and lock file. The prompt can reason over the full dependency graph and propose a minimal set of version changes. Guardrail: Always feed the lock file and the exact error output; never rely on the model's memory of package versions.

02

Bad Fit: Undefined or Custom Registries

Avoid when: The project uses private registries, git dependencies, or local path references that the model cannot inspect. The prompt will hallucinate version availability or registry metadata. Guardrail: Gate the prompt behind a registry reachability check; if the model cannot query the registry, escalate to a human with access.

03

Required Inputs: Lock File and Full Error Trace

Risk: Without the lock file, the model guesses transitive constraints and produces version sets that fail on re-lock. Without the full error trace, it misses the root conflict. Guardrail: Build a harness that concatenates [LOCK_FILE], [MANIFEST], and [ERROR_OUTPUT] into a single structured prompt; reject requests missing any of the three.

04

Operational Risk: Silent Transitive Breakage

Risk: The model proposes a top-level version bump that resolves the direct conflict but introduces a breaking change in a transitive dependency not covered by the error trace. Guardrail: After applying the suggestion, run the full test suite and a lock file regenerate + diff step; flag any new deprecation warnings or peer dependency shifts for human review.

05

Operational Risk: Unbounded Retry Loops

Risk: A CI pipeline that auto-applies the prompt's output can enter a loop where each fix creates a new conflict, burning compute and delaying the build. Guardrail: Cap retries at 3 attempts with escalating context (add the previous suggestion and its failure to the next prompt); after the cap, stop and open a ticket with the full attempt history.

06

Operational Risk: Ecosystem-Specific Semantics

Risk: Version resolution rules differ across ecosystems (npm overrides, pip extras, Maven scope, Cargo features). A generic prompt may apply the wrong conflict strategy. Guardrail: Include an [ECOSYSTEM] field in the prompt template that switches resolution logic; validate the output against ecosystem-specific lint rules before applying.

PROMPT PLAYBOOK

Copy-Ready Prompt Template

A reusable prompt that resolves dependency version conflicts by analyzing error output and proposing a compatible version set.

This template is designed to be pasted directly into your AI harness. It accepts a dependency manifest, a package manager error log, and optional constraints to produce a resolved version set. The prompt instructs the model to reason about transitive dependencies, explain its resolution strategy, and output a machine-readable lock file diff. All placeholders are enclosed in square brackets and must be replaced with concrete values before execution.

text
You are a dependency resolution engineer. Your task is to resolve a version conflict reported by a package manager.

## INPUT
- Package manager: [PACKAGE_MANAGER]
- Dependency manifest file content:
  ```[MANIFEST_FORMAT]
  [MANIFEST_CONTENT]
  • Error output from the package manager:
    code
    [ERROR_LOG]

CONSTRAINTS

  • [CONSTRAINTS]

OUTPUT_SCHEMA

Return a JSON object with the following structure: { "resolution_strategy": "string explaining the approach taken", "conflict_analysis": { "root_cause": "string identifying the primary conflict", "affected_packages": ["package@version", ...], "constraint_paths": ["dependency -> parent -> root", ...] }, "resolved_versions": { "[PACKAGE_NAME]": "[RESOLVED_VERSION]", ... }, "lock_file_diff": "string showing the minimal changes to the lock file", "transitive_consistency_check": { "passed": true, "notes": "string explaining why the set is consistent" }, "regeneration_command": "string with the exact command to regenerate the lock file" }

INSTRUCTIONS

  1. Parse the error log to identify the exact conflicting packages and their version constraints.
  2. Trace the dependency graph to find all constraint paths leading to the conflict.
  3. Propose a compatible version set that satisfies all direct and transitive constraints.
  4. If no compatible set exists, explain the unresolvable conflict and suggest the minimal set of constraint relaxations required.
  5. Ensure the resolved versions are consistent across all transitive dependencies.
  6. Output only the JSON object. Do not include any other text.

To adapt this template, replace each placeholder with concrete values. [PACKAGE_MANAGER] should be one of npm, pip, maven, or cargo. [MANIFEST_FORMAT] should be the file type such as json, yaml, toml, or xml. [CONSTRAINTS] can include directives like "Do not upgrade major versions" or "Prefer the latest patch within the current minor range." For high-risk production environments, add a [RISK_LEVEL] field and require a human review step before applying the lock file diff. Always validate the regenerated lock file in a CI sandbox before merging.

IMPLEMENTATION TABLE

Prompt Variables

Required inputs for the Dependency Version Conflict Resolution Prompt. Each placeholder must be populated before the prompt is sent. Validation notes describe how the harness should check input quality before execution.

PlaceholderPurposeExampleValidation Notes

[DEPENDENCY_FILE]

The full content of the dependency manifest file exhibiting the conflict

package.json, requirements.txt, pom.xml, Cargo.toml

Parse check: must be valid JSON, TOML, or XML per file extension. Reject if empty or unparseable.

[LOCK_FILE]

The current lock file content, if one exists, showing the resolved dependency tree before the conflict

package-lock.json, Pipfile.lock, Cargo.lock

Parse check: must be valid structured format matching [DEPENDENCY_FILE] ecosystem. Null allowed if no lock file exists.

[CONFLICT_ERROR]

The raw error output from the package manager or build tool describing the version conflict

npm ERR! ERESOLVE unable to resolve dependency tree...

String check: must contain a recognized conflict keyword (conflict, incompatible, requires, ERESOLVE). Reject if error is unrelated (network timeout, auth failure).

[TARGET_ACTION]

The intended package operation that triggered the conflict

install, update, add react@18, pip install --upgrade

Enum check: must be one of install, update, add, upgrade, remove. Reject if missing or unrecognized.

[PACKAGE_MANAGER]

The package manager and version in use

npm@10.2.0, pip@23.3.1, mvn@3.9.5, cargo@1.74.0

Format check: must match pattern name@semver. Reject if version is missing or manager is unsupported.

[RUNTIME_CONSTRAINTS]

Any additional constraints such as Node.js version, Python version, or OS platform

node>=18.0.0, python==3.11, linux/amd64

Schema check: must be a list of key-value pairs or a valid constraints object. Null allowed if no extra constraints.

[RESOLUTION_STRATEGY]

The preferred strategy for resolving conflicts

newest, minimal-change, security-first, direct-only

Enum check: must be one of newest, minimal-change, security-first, direct-only. Default to minimal-change if null.

PROMPT PLAYBOOK

Implementation Harness Notes

How to wire the dependency version conflict resolution prompt into a CI pipeline, CLI tool, or developer workflow with validation, retries, and lock file regeneration.

This prompt is designed to be called programmatically from a build pipeline, pre-commit hook, or developer CLI. The harness should capture the raw package manager error output (stdout/stderr) and the full dependency manifest file as the two primary inputs. For npm, this means package.json plus the conflict error from npm install. For pip, requirements.txt or pyproject.toml plus the pip install resolution failure. For Maven, pom.xml plus the dependency tree conflict. For Cargo, Cargo.toml plus the resolver error. The harness must not truncate error output—transitive dependency conflicts often appear deep in the error trace, and cutting it short will cause the model to miss the root cause.

After the model returns a proposed version set, the harness must validate the output before applying it. Parse the response for a structured version map (e.g., {"package": "version"}) and run a dry-run install using the package manager's native resolver. For npm, execute npm install --dry-run after applying the suggested versions to package.json. For pip, use pip install --dry-run with the proposed pinned versions. If the dry-run fails, feed the new error back into the prompt as a retry with the previous suggestion included as context. Implement a retry budget of 3 attempts before escalating to a human. Log each attempt's input, output, and dry-run result for auditability. The harness should also regenerate the lock file (package-lock.json, Pipfile.lock, pom.xml with resolved versions, Cargo.lock) only after a successful dry-run, and commit the change with a message that cites the original conflict error.

For high-risk environments (production deployments, monorepos with shared dependencies, regulated build pipelines), require human approval before applying the suggested version changes. The harness should surface a diff of the dependency manifest changes alongside the model's explanation and the dry-run result. Do not auto-merge version conflict resolutions that change major versions or remove pinned transitive dependencies without review. Avoid wiring this prompt into fully automated CD pipelines without a manual gate—version resolution errors can introduce subtle runtime incompatibilities that pass a dry-run but fail integration tests. Pair this prompt with a subsequent test suite run as a required validation step before the change is accepted.

IMPLEMENTATION TABLE

Expected Output Contract

Defines the required structure, types, and validation rules for the model's response when resolving dependency version conflicts. Use this contract to parse, validate, and integrate the output into your CI/CD pipeline or automated remediation workflow.

Field or ElementType or FormatRequiredValidation Rule

resolution_summary

string

Must be a non-empty string under 500 characters. Check for the presence of a specific version set or a clear statement that no resolution was found.

compatible_versions

array of objects

Array must contain at least one object. Each object must have 'package_name' (string) and 'resolved_version' (string, valid semver). Validate semver format and check for duplicates.

compatible_versions[].package_name

string

Must exactly match a package name from the [DEPENDENCY_FILE] input. Perform a case-sensitive string match against the input manifest.

compatible_versions[].resolved_version

string

Must be a valid semantic version string (e.g., '2.1.0'). Validate against the semver regex. Must not be a version range or a wildcard.

conflict_chain

array of strings

Must contain at least one string describing the dependency path to the conflict. Each string should follow the format 'parent_pkg -> conflicting_pkg (required_version)'. Validate the presence of '->'.

transitive_impact

array of objects

If present, each object must have 'package_name' (string) and 'previous_version' (string, valid semver). Validate that 'package_name' exists in the input dependency graph. Null allowed if no transitive changes.

lock_file_regeneration_hint

string

Must be a non-empty string containing the exact CLI command to regenerate the lock file (e.g., 'npm install', 'pip freeze > requirements.txt'). Validate that the command matches the package manager detected from [PACKAGE_MANAGER].

unresolvable_constraints

array of strings

If no resolution is found, this array must be present and contain at least one string explaining the blocking constraint. If a resolution is found, this field must be null. Validate mutual exclusivity with 'compatible_versions'.

PRACTICAL GUARDRAILS

Common Failure Modes

Dependency version resolution is a combinatorial problem where the model must satisfy constraints across direct, transitive, and lockfile dependencies. These are the most common ways the prompt fails in production and how to guard against them.

01

Transitive Conflict Blindness

What to watch: The model resolves direct dependency conflicts but ignores transitive dependencies, producing a version set that passes top-level checks but fails deeper in the tree. Guardrail: Require the prompt to output a full dependency tree, not just top-level versions. Validate with pip check or npm ls before accepting the result.

02

Lockfile Regeneration Mismatch

What to watch: The model proposes version changes that look valid but produce a different lockfile hash than expected, breaking reproducible builds. Guardrail: Run npm install --package-lock-only or pip freeze after applying the suggestion and diff the result against the expected lockfile. Reject if hashes diverge.

03

Peer Dependency Override Errors

What to watch: The model suggests --force or --legacy-peer-deps flags as a quick fix without understanding the runtime implications, masking real incompatibilities. Guardrail: Add a constraint that forbids suggesting force flags unless the prompt explicitly lists the peer conflict and explains why it is safe to ignore at runtime.

04

Ecosystem-Specific Syntax Confusion

What to watch: The model applies npm-style caret ranges to pip constraints or Maven version ranges to Cargo, producing invalid syntax that the package manager rejects. Guardrail: Include the package manager name and version as a required input field. Validate the output against the ecosystem's version spec grammar before retrying.

05

Pre-Release and Build Metadata Drift

What to watch: The model selects alpha, beta, or release-candidate versions without flagging them, introducing unstable dependencies into production pipelines. Guardrail: Add a [STABILITY_POLICY] input that specifies allowed channels. Post-process the output to reject any version containing pre-release tags unless explicitly permitted.

06

Single-Registry Assumption Failure

What to watch: The model assumes all packages come from the default public registry and ignores private registries, mirrors, or scoped packages, producing unresolvable version suggestions. Guardrail: Require [REGISTRY_CONFIG] as an input block. Validate that every suggested package version exists in the specified registry before presenting the resolution.

IMPLEMENTATION TABLE

Evaluation Rubric

Criteria for testing the Dependency Version Conflict Resolution Prompt before deployment. Each row defines a pass standard, a failure signal, and a concrete test method to validate output quality.

CriterionPass StandardFailure SignalTest Method

Lock File Regeneration

Running the package manager's install command with the proposed versions generates a new lock file without errors.

Package manager exits with a non-zero code or reports an unresolved conflict.

Execute npm install, pip install, or equivalent in a sandboxed environment using the output version set and verify exit code 0.

Transitive Dependency Consistency

All direct and transitive dependencies in the proposed set satisfy the stated version constraints of their dependents.

A dependency is resolved to a version outside the range required by its parent package.

Parse the dependency tree output after install and run a constraint satisfaction check against the original manifest requirements.

Conflict Error Resolution

The specific conflict reported in [ERROR_LOG] is no longer present in the new dependency graph.

The original error message or an equivalent version-conflict error reappears.

Diff the error output from the original failed install against the error output from the install using the proposed resolution.

Semantic Versioning Adherence

Proposed version bumps respect semantic versioning rules: no breaking changes in a minor or patch bump unless explicitly required.

A package is upgraded across a major version boundary without justification or a corresponding changelog reference.

Parse the proposed version changes and flag any major version increments. Require a justification string in the output for each.

Output Schema Validity

The response contains a valid JSON object with the required [OUTPUT_SCHEMA] fields: resolution_strategy, version_overrides, and breaking_changes.

The response is missing a required field, contains extra untyped fields, or fails JSON parsing.

Validate the raw model output against the defined JSON Schema using a programmatic validator before any downstream processing.

Direct Dependency Preservation

All packages explicitly listed in [DEPENDENCY_FILE] remain in the resolved set with a valid version.

A direct dependency is removed or downgraded to an incompatible version as a side effect of resolving a conflict.

Extract the list of direct dependencies from the input file and assert each is present in the version_overrides output with a non-null version.

Rollback Safety

The proposed resolution does not downgrade a package to a version with a known critical security vulnerability.

A package is downgraded to a version flagged in a configured advisory database (e.g., GitHub Advisory, OSV).

Cross-reference proposed versions against a local or API-based vulnerability database and fail the test if any match a critical advisory.

ADAPTATION OPTIONS

Adapt This Prompt

How to adapt

Use the base prompt with a single dependency file and error message. Skip lock file regeneration checks and focus on getting a plausible version set. Accept the first valid-looking resolution without deep transitive validation.

code
System: You are a dependency resolver. Given [DEPENDENCY_FILE] and [ERROR_MESSAGE], propose a compatible version set.

Constraints: [CONSTRAINTS]

Watch for

  • Suggested versions that resolve the direct conflict but break transitive dependencies
  • Missing peer dependency checks
  • Overly broad version ranges that reintroduce the conflict on next install
Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.