The 7 essential patterns for developing GenAI products in 2025

Moving from generative AI prototypes to production systems is a major challenge for development teams. Unlike traditional applications, GenAI systems introduce unique issues such as hallucinations, uncontrolled data access, and non-deterministic behavior. In this article, we analyze the emerging patterns that enable building robust and reliable GenAI products in 2025.

The challenge of moving GenAI systems to production

The transition from proof of concept to production GenAI applications is not simply an extension of traditional transactional or analytical systems. Teams that succeed in this transition have identified recurring patterns to overcome the challenges specific to this technology. These patterns are not universal standards, but rather proven approaches that can be adapted depending on the context.

With the rapid evolution of tools and techniques in this field, it is crucial to understand when and how to apply these patterns to maximize their effectiveness. Let's look at the most important approaches emerging in 2025.

Direct Prompting: the starting point

Direct Prompting connects a user directly to a language model (LLM) without intermediate steps. The user sends text queries and receives responses generated by the model.

This approach, while simple, has significant limitations. The model is constrained by the data it was trained on, meaning it doesn't know about events after its training and lacks context specific to your domain.

Moreover, models can display excessive confidence even when their knowledge is limited, inventing plausible but incorrect answers. This behavior becomes problematic when the LLM represents an organization and must provide reliable information.

Evals: systematically evaluating performance

In traditional software development, we use tests to verify that our systems work as expected. With non-deterministic GenAI systems, we need a different approach: Evals.

Evals evaluate an LLM's responses in the context of a specific task. Instead of examining individual outputs, we typically evaluate the model's behavior on a set of scenarios to ensure it meets the desired standards.

Scoring and evaluation methods

There are different evaluation techniques depending on who calculates the score:

• Self-evaluation: The LLM evaluates its own responses. This approach has significant risks because if the model's self-evaluation process is flawed, it can produce results that appear more confident than they really are.
• LLM as judge: Outputs are evaluated by another model, either a more powerful LLM or a specialized Small Language Model (SLM). This technique has become popular for automating the evaluation process.
• Human evaluation: Humans manually check whether responses match the desired tone, style, and intent. Although difficult to scale, it's the most effective method for evaluating qualitative aspects.

In practice, combining an LLM as judge with human evaluation yields the best results for understanding overall system performance.

Embeddings: vector representation of data

Embeddings transform large blocks of data into numerical vectors such that embeddings close to each other represent related concepts. This technique is fundamental for many advanced GenAI patterns.

Embeddings allow representing texts, images, or other data types in a vector space where proximity indicates semantic similarity. This representation facilitates searching for similar content and understanding relationships between different elements.

For example, image embeddings allow searching for visually similar images, while text embeddings facilitate semantic search beyond simple keyword matching. In LLMs, embeddings are used to represent context and understand relationships between concepts.

Retrieval Augmented Generation (RAG): enriching LLM knowledge

Retrieval Augmented Generation (RAG) is a pattern that retrieves relevant document fragments and includes them when sending prompts to the LLM. This approach overcomes the limitations of the model's static knowledge by providing specific contextual information.

RAG is particularly useful for:

• Providing recent information the model doesn't know
• Adding domain- or organization-specific knowledge
• Reducing hallucinations by anchoring responses in reliable sources

While basic RAG is powerful, it has certain limitations that require complementary patterns to be fully effective.

The RAG template in practice

A typical RAG system follows this flow:

1. The user submits a query
2. The system searches for relevant documents in a knowledge base
3. The most relevant fragments are extracted
4. These fragments are added to the prompt sent to the LLM
5. The LLM generates a response informed by this additional knowledge

However, to build a robust RAG system, several complementary patterns are needed.

Complementary patterns to improve RAG

Hybrid Retriever: combining different search techniques

Hybrid Retriever combines embedding search with other search techniques to improve result relevance. This approach overcomes the limitations of a single search method.

For example, you can combine:

• Embedding search for semantic understanding
• Keyword search for terminological precision
• Metadata search for contextual filtering

This hybrid approach is particularly effective when data is heterogeneous or when different query types require different search strategies.

Query Rewriting: reformulating for better search

Query Rewriting uses an LLM to create multiple alternative formulations of a query and perform searches with all these variants. This technique significantly improves search result quality.

Often, users phrase their questions in ways that don't directly match the terms used in documents. Query Rewriting bridges this gap by generating variations more likely to match stored content.

This approach is especially useful for chatbots and AI assistants that must correctly interpret user intent despite varied phrasing.

Reranker: refining document selection

Reranker ranks a set of retrieved document fragments by usefulness and sends the best ones to the LLM. This extra step optimizes the quality of context provided to the model.

After initial document retrieval, the Reranker evaluates each fragment based on its relevance to the specific query. This finer evaluation selects only the most useful information, reducing noise and optimizing the LLM's limited context.

Guardrails: securing inputs and outputs

Guardrails use separate LLM calls to prevent dangerous inputs or sanitize results. This pattern is essential for creating safe and reliable GenAI systems.

There are three main approaches to implementing guardrails:

• LLM-based guardrails: Use a separate LLM to assess whether an input or output is appropriate
• Embedding-based guardrails: Compare embeddings of inputs/outputs with known examples of problematic content
• Rule-based guardrails: Apply predefined rules to filter inappropriate content

This protection layer is especially important for consumer-facing applications where users might try to exploit system vulnerabilities.

Fine Tuning: when RAG isn't enough

Fine Tuning involves additional training on a pre-trained LLM to improve its knowledge base in a particular context. This approach becomes relevant when RAG reaches its limits.

Fine Tuning is particularly useful in the following scenarios:

• When the model must consistently adopt a specific style or tone
• For use cases requiring deep understanding of a specialized domain
• When performance needs to be optimized for specific repetitive tasks
• To reduce latency by embedding knowledge directly into the model rather than via RAG

However, Fine Tuning requires more resources and expertise than RAG, and should be considered a complementary rather than alternative solution.

Building a realistic RAG system in 2025

To deploy a robust RAG system in production, it's usually necessary to combine several of the mentioned patterns. A typical architecture might look like this:

1. The user submits a query
2. Guardrails check that the query is appropriate
3. The query is rewritten (Query Rewriting) to generate multiple variants
4. A Hybrid Retriever searches for relevant documents
5. A Reranker refines the selection of the most useful fragments
6. The selected fragments are integrated into the prompt sent to the LLM
7. The generated response is checked by guardrails before being presented to the user
8. Evals are run regularly to monitor system performance

This multi-layered approach creates robust GenAI systems that provide accurate, relevant, and secure responses.

Conclusion: the future of GenAI systems

The patterns presented in this article represent the current state of best practices for developing GenAI products in 2025. However, this field evolves rapidly, and new patterns will continue to emerge as we gain more experience.

The future of GenAI systems lies in the intelligent integration of these patterns to create architectures tailored to each specific use case. Teams that master these patterns and understand when to apply them will have a significant advantage in developing robust and high-performing GenAI products.

Ultimately, success in this field depends not only on technology, but also on understanding the nuances of these non-deterministic systems and the ability to combine appropriate patterns to meet the specific needs of each application.