This paper proposes a novel technique to prove a one - shot version of achievability results in network information theory . The technique is not based on covering and packing lemmas. In this technique , we use a stochastic encoder and decoder with a particular structure for coding that resembles both the ML and the joint-typicality coders. Although stochastic encoders and decoders do not usually enhance the capacity region, their use simplifies the analysis. The Jensen inequality lies at the heart of error analysis, which enables us to deal with the expectation of many terms coming from stochastic encoders and decoders at once. The technique is illustrated via four examples: point-to-point channel coding, Gelfand-Pinsker, broadcast channel and Berger-Tung problem of distributed lossy compression. Applying the one - shot result for the memoryless broadcast channel in the asymptotic case, we get the entire region of Marton's inner bound without any need for time-sharing. Also, these results are employed in conjunction with multi-dimensional berry-esseen CLT to derive new regions for finite-blocklength regime of Gelfand-Pinsker.