How to Properly Create a Technical Specification for an AI Project

A technical specification (TS) is a fundamental document for any IT project, but the specifics of AI require a fundamentally different approach to its development.
The key difference lies in the focus. A traditional TS emphasizes logic, functionality and interfaces, where a deterministic result is expected (one input always produces the same output). However, a TS for an AI/ML project focuses on data, evaluation metrics and probability. It must define what is considered a “correct” answer and minimize errors.
Thus, the client’s task when preparing a TS is not to detail every function, but to define business goals, clear criteria for measuring model success and ensure access to high quality data.

Concept Brief — from Business Idea to Technical Specification

1. Clear Definition of the Business Problem and Value

A technical specification should always begin by answering the question: “Why are we investing in AI?” Whether it is to optimize existing processes, expand service or sales delivery methods and platforms, or create a standalone product or startup for commercial purposes.

The technical specification must transform business goals into measurable business KPIs that the AI system will directly influence. For example, if the goal is process optimization, a KPI could be “reduce manual document processing time by 40%.” If the goal is sales expansion, a KPI could be “increase the conversion rate of personalized recommendations by 15%.”

The product owner should focus on defining functional requirements that directly support specific, measurable business goals.

2. Phased Approach: PoC, MVP, and Scalability Readiness

A technical specification must include an initial proof of concept (PoC) phase. This determines whether it is even possible to achieve a minimal result using available data and technologies.

Defining PoC success criteria is critically important. The specification must clearly establish a minimum threshold for an ML metric (for example, “at least 65 percent F1 score”) that confirms further investment in developing a full minimum viable product (MVP) is justified. If this threshold is not reached, the project must be reviewed, its strategy revised, or it should be discontinued.

This approach ensures that investments are allocated gradually. It prevents premature funding before technological feasibility is validated. If a specification ignores the PoC phase, it automatically becomes a financially risky document.

3. Data Quality and Management

Data in AI projects is not just input parameters; it is the raw material that determines the quality of the final product. For this reason, its technical specification is often more detailed and important than for code.

The success of any machine learning project depends on the availability of accurate and well annotated data. It directly affects model performance. Excessive focus on collecting large volumes of data at the expense of quality can complicate the system and increase the risk of errors. Transparency about the data being used helps prevent potential rights violations.

Training data should closely reflect the reality in which the model will operate. The specification requires the ML team to conduct a detailed analysis of the dataset to ensure alignment with the target environment. It is also essential to establish requirements for data structure and cleanliness so AI algorithms can process it effectively.

Data labeling (annotation) is the foundation for training most ML models. Labeled data enables models to understand relationships among different data points and make informed decisions.

The technical specification must describe in detail how data will be annotated. If traditional ML methods or LLMs (Large Language Models) requiring fine tuning are used, high quality annotation is mandatory. The specification should answer the following questions:

• Who will annotate the data? Are domain experts required (for example, doctors or lawyers)?
  
• How will annotation quality be controlled? For instance, will an Inter Annotator Agreement metric be used to measure consistency between annotators?
  
• What methodology will be applied? Will annotation be manual, or will semi automated approaches be used where ML models assist in labeling after being trained on a small human annotated subset and gradually improve their confidence?
  

Leaders who only have experience in classical IT projects often assume that data already exists in the required format. Insufficient attention to annotation quality or neglecting this phase results in even the most advanced model architecture learning from errors, which guarantees project failure at the MVP stage.

Another element of the specification is the requirement for AI system fairness. Therefore an assessment stage for potential bias must be mandatory. For example, a model can become discriminatory if the training dataset unevenly represents certain demographic groups.

4. Turning Technical Risk into Metrics

For audiences that are not ML specialists, Accuracy often seems like the simplest metric. It measures the overall share of correct classifications, both positive and negative.

Accuracy can serve as a rough estimate of model quality on balanced datasets. However, in most real world scenarios where data is imbalanced (for example, only one percent of cases are fraud and ninety nine percent are legitimate) or where one type of error is significantly more costly than another, Accuracy is misleading. For instance, a model that always predicts “Negative” for a highly imbalanced dataset (one percent positive) may reach ninety nine percent Accuracy while missing all important positive cases and being completely useless for the business.

The specification must clearly define which type of error carries higher cost for the business:

• False Negative (FN): The model incorrectly classified a positive case as negative (for example, it failed to detect fraud or missed a disease). If the cost of FN is high, the specification should require optimization of Recall. Recall shows what proportion of truly positive cases the model was able to detect.
• False Positive (FP): The model incorrectly classified a negative case as positive (for example, flagged a legitimate transaction as fraudulent or blocked a valid user). If the cost of FP is high (for instance, it leads to loss of a customer), the specification should require optimization of Precision. Precision shows what proportion of predictions labeled as positive were actually correct.
• In most cases a balance between Precision and Recall is required. This is where the F1 Score is used. It is a harmonic mean between Precision and Recall, requiring the model not only to find positive cases but to do so with high reliability.

Other important metrics used for assessing the model’s ability to distinguish between classes include ROC AUC and PR AUC. AUC stands for “area under the curve.” These metrics evaluate performance across all possible classification thresholds. ROC AUC and PR AUC are useful for comparing the overall effectiveness of different models.

The choice of metric directly reflects the definition of business risk. If the specification requires a high Recall (for example, ninety eight percent in a security system), it means the business consciously accepts a higher level of false positives (false alarms). This requires allocating additional resources (people or systems) for manual verification of these false triggers. The specification must force the client to analyze the cost of each error type. Choosing the wrong metric leads the team to optimize the model for an irrelevant indicator, resulting in significant financial losses or reputational damage.

It is also important to note that metrics such as Precision, Recall and Accuracy are sensitive to the classification threshold. The specification must not only require a target metric but also expect the ML team to determine the optimal classification threshold (for example, a probability threshold of zero point forty five instead of the standard zero point five) that maximizes financial or operational value for the business.

The requirement for the specification is to establish clear, measurable goals for the chosen metric.

1. Setting the Baseline: The ML team must determine the minimum performance level that the developed model must exceed. This baseline may be based on human performance, simple statistical algorithms or even random guessing. If the model cannot surpass the baseline, the project is not viable.
2. Setting the Target: This is a clearly defined minimum metric value (for example, “the F1 Score must be at least zero point eighty eight”) that is considered sufficient for commercial deployment and achieving business KPIs.

The specification must clearly separate infrastructure requirements for different stages of the model lifecycle:

Experimentation Environment: A space where ML engineers can freely conduct research, test hypotheses, and train models. The specification should require a separate repository for these experiments.

Serving Environment (Production): A space where the final model is deployed and provides predictions to users. A separate repository is required for serving.

If there is no separation between experimental and production environments, it becomes impossible to quickly roll back changes or diagnose errors in real time, undermining trust in the AI system. The specification must ensure that experiment results can be easily verified and the model can be accurately reproduced, requiring versioning of code, data, and the model itself.

• It is essential to clearly define integration and infrastructure requirements. The specification should indicate whether the system will be hosted on an existing VPS or hosting service, or if it will require migration to a new server or hosting environment. These details are critical for successful deployment and maintaining stability.
• AI models are not static. Their performance degrades over time. The specification must include requirements for:
• Model Performance Monitoring: Tracking selected ML metrics in real time.
• Drift Detection: Mechanisms to detect Data Drift (changes in input data over time that differ from the training data) or Model Drift (decline in predictive performance). These mechanisms are mandatory to ensure the long term value of the project.

While the client should focus on business goals, avoiding common mistakes ensures effective collaboration with the technical team. One of the most frequent client errors is failing to define integration and infrastructure requirements. This leads to the so called “model on a laptop” problem: the model works perfectly in the ML engineer’s lab but cannot be deployed because there are no clear serving plans or infrastructure specifications.

Second, the specification should not dictate a specific algorithm (“use only Random Forest” or “use a specific LLM”). Instead, it should define the target metric and performance. Requiring a particular algorithm without validating its suitability can unnecessarily constrain the technical team, forcing them to use a suboptimal solution.

Third, the absence of requirements for versioning data, models, and experiments creates chaos. This makes it impossible to reproduce previous results, which is critical for regulatory compliance and auditing.

Final Checklist and Recommendations

Creating a technical specification for an AI project requires an interdisciplinary approach. To verify the completeness and quality of the document, a structured checklist is recommended:

• Business goals must be directly linked to optimizing or expanding services.
  
• Data quality and annotation (labeling) are fundamental requirements. Prioritize quality over quantity.
  
• The choice of metric should reflect the cost of false positives and false negatives (FP/FN).
  
• A phased approach (PoC → MVP) is mandatory to validate feasibility and reduce financial risk.
  
• Separate repositories for experiments and serving are required, along with clear hosting plans.

A technical specification for AI is not a final, static document. It requires continuous dialogue among all stakeholders.

Unlike traditional IT projects, where the specification often serves as a final contract, in ML projects the results of the initial PoC phase may necessitate revisiting even the most fundamental business goals. The team may find that the desired F1 Score of 0.90 is unattainable with the available data, and only an F1 Score of 0.82 is achievable. Therefore, the specification should include a mechanism for jointly reviewing target metrics and business value.

Leaders initiating an AI project must be able to explain to their stakeholders why they chose F1 Score or Recall. If the choice of the key metric cannot be explained in simple terms as a safeguard against a specific business risk, the specification is either unclear or the metric choice is suboptimal.

Therefore, a technical specification for an AI project is a unique document that transforms the uncertainty of scientific research into measurable business outcomes. Project success does not depend on the code, but on the client’s ability to clearly articulate business goals, convert risks into technical metrics, and provide high quality, annotated raw data. Using a phased approach starting with a PoC, and consciously selecting metrics that reflect the cost of errors, are fundamental principles for ensuring the financial and technical success of AI investments.